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Carbon fiber plain-woven prepreg is one of the basic materials in the field of composite material design and manufacturing, in which defect identification is an important and easily neglected part of testing. Here, a novel high recognition rate inspection method for carbon fiber plain-woven prepregs is proposed for inspecting bubble and wrinkle defects based on image texture feature compression. The proposed method attempts to divide the image into non-overlapping block lattices as texture primitives and compress them into a binary feature matrix. Texture features are extracted using a gray level co-occurrence matrix. The defect types are further defined according to texture features by k-means clustering. The performance is evaluated in some existing computer vision and machine learning methods based on fiber recognition. By comparing the result, an overall recognition rate of 0.944 is achieved, which is competitive with the state-of-the-arts. [ABSTRACT FROM AUTHOR]

This study aimed to identify a distant-recurrence image biomarker in NSCLC by investigating correlations between heterogeneity functional gene expression and fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) image features of NSCLC patients. RNA-sequencing data and 18F-FDG PET images of 53 patients with NSCLC (19 with distant recurrence and 34 without recurrence) from The Cancer Imaging Archive and The Cancer Genome Atlas Program databases were used in a combined analysis. Weighted correlation network analysis was performed to identify gene groups related to distant recurrence. Genes were selected for functions related to distant recurrence. In total, 47 image features were extracted from PET images as radiomics. The relationship between gene expression and image features was estimated using a hypergeometric distribution test with the Pearson correlation method. The distant recurrence prediction model was validated by a random forest (RF) algorithm using image texture features and related gene expression. In total, 37 gene modules were identified by gene-expression pattern with weighted gene co-expression network analysis. The gene modules with the highest significance were selected (p-value < 0.05). Nine genes with high protein–protein interaction and area under the curve (AUC) were identified as hub genes involved in the proliferation function, which plays an important role in distant recurrence of cancer. Four image features (GLRLM_SRHGE, GLRLM_HGRE, SUVmean, and GLZLM_GLNU) and six genes were identified to be correlated (p-value < 0.1). AUCs (accuracy: 0.59, AUC: 0.729) from the 47 image texture features and AUCs (accuracy: 0.767, AUC: 0.808) from hub genes were calculated using the RF algorithm. AUCs (accuracy: 0.783, AUC: 0.912) from the four image texture features and six correlated genes and AUCs (accuracy: 0.738, AUC: 0.779) from only the four image texture features were calculated using the RF algorithm. The four image texture features validated by heterogeneity group gene expression were found to be related to cancer heterogeneity. The identification of these image texture features demonstrated that advanced prediction of NSCLC distant recurrence is possible using the image biomarker. [ABSTRACT FROM AUTHOR]

Image processing has played a relevant role in various industries, where the main challenge is to extract specific features from images. Specifically, texture characterizes the phenomenon of the occurrence of a pattern along the spatial distribution, taking into account the intensities of the pixels for which it has been applied in classification and segmentation tasks. Therefore, several feature extraction methods have been proposed in recent decades, but few of them rely on entropy, which is a measure of uncertainty. Moreover, entropy algorithms have been little explored in bidimensional data. Nevertheless, there is a growing interest in developing algorithms to solve current limits, since Shannon Entropy does not consider spatial information, and SampEn2D generates unreliable values in small sizes. We introduce a proposed algorithm, EspEn (Espinosa Entropy), to measure the irregularity present in two-dimensional data, where the calculation requires setting the parameters as follows: m (length of square window), r (tolerance threshold), and ρ (percentage of similarity). Three experiments were performed; the first two were on simulated images contaminated with different noise levels. The last experiment was with grayscale images from the Normalized Brodatz Texture database (NBT). First, we compared the performance of EspEn against the entropy of Shannon and SampEn2D. Second, we evaluated the dependence of EspEn on variations of the values of the parameters m, r, and ρ. Third, we evaluated the EspEn algorithm on NBT images. The results revealed that EspEn could discriminate images with different size and degrees of noise. Finally, EspEn provides an alternative algorithm to quantify the irregularity in 2D data; the recommended parameters for better performance are m = 3, r = 20, and ρ = 0.7. [ABSTRACT FROM AUTHOR]

The differences in sensor imaging mechanisms, observation angles, and scattering characteristics of terrestrial objects significantly limit the registration performance of synthetic aperture radar (SAR) and optical heterologous images. Traditional methods particularly struggle in weak feature regions, such as harbors and islands with substantial water coverage, as well as in desolate areas like deserts. This paper introduces a robust heterologous image registration technique based on region-adaptive keypoint selection that integrates image texture features, targeting two pivotal aspects: feature point extraction and matching point screening. Initially, a dual threshold criterion based on block region information entropy and variance products effectively identifies weak feature regions. Subsequently, it constructs feature descriptors to generate similarity maps, combining histogram parameter skewness with non-maximum suppression (NMS) to enhance matching point accuracy. Extensive experiments have been conducted on conventional SAR-optical datasets and typical SAR-optical images with different weak feature regions to assess the method's performance. The findings indicate that this method successfully removes outliers in weak feature regions and completes the registration task of SAR and optical images with weak feature regions. [ABSTRACT FROM AUTHOR]

Image denoising, as an essential component of image pre-processing, effectively reduces noise interference to enhance image quality, a factor of considerable research importance. Traditional denoising methods often lead to the blurring of image details and a lack of realism at the image edges. To deal with these issues, we propose an image denoising algorithm named Residual structure and Cooperative Attention mechanism based on Generative Adversarial Networks (RCA-GAN). This algorithm proficiently reduces noise while focusing on preserving image texture details. To maximize feature extraction, this model first employs residual learning within a portion of the generator's backbone, conducting extensive multi-dimensional feature extraction to preserve a greater amount of image details. Secondly, it introduces a simple yet efficient cooperative attention module to enhance the representation capacity of edge and texture features, further enhancing the preservation of intricate image details. Finally, this paper constructs a novel loss function—the Multimodal Loss Function—for the network training process. The experimental results were evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) as evaluation metrics. The experimental results demonstrate that the proposed RCA-GAN image denoising algorithm has increased the average PSNR from 24.71 dB to 33.76 dB, achieving a 36.6% improvement. Additionally, the average SSIM value has risen from 0.8451 to 0.9503, indicating a 12.4% enhancement. It achieves superior visual outcomes, showcasing the ability to preserve image texture details to a greater extent and excel in edge preservation and noise suppression. [ABSTRACT FROM AUTHOR]

Faced with the problem of incompatibility between traditional information acquisition mode and spaceborne earth observation tasks, starting from the general mathematical model of compressed sensing, a theoretical model of block compressed sensing was established, and a full-process adaptive coding and decoding compressed sensing framework for remote sensing images was proposed, which includes five parts: mode selection, feature factor extraction, adaptive shape segmentation, adaptive sampling rate allocation and image reconstruction. Unlike previous semi-adaptive or local adaptive methods, the advantages of the adaptive encoding and decoding method proposed in this paper are mainly reflected in four aspects: (1) Ability to select encoding modes based on image content, and maximizing the use of the richness of the image to select appropriate sampling methods; (2) Capable of utilizing image texture details for adaptive segmentation, effectively separating complex and smooth regions; (3) Being able to detect the sparsity of encoding blocks and adaptively allocate sampling rates to fully explore the compressibility of images; (4) The reconstruction matrix can be adaptively selected based on the size of the encoding block to alleviate block artifacts caused by non-stationary characteristics of the image. Experimental results show that the method proposed in this article has good stability for remote sensing images with complex edge textures, with the peak signal-to-noise ratio and structural similarity remaining above 35 dB and 0.8. Moreover, especially for ocean images with relatively simple image content, when the sampling rate is 0.26, the peak signal-to-noise ratio reaches 50.8 dB, and the structural similarity is 0.99. In addition, the recovered images have the smallest BRISQUE value, with better clarity and less distortion. In the subjective aspect, the reconstructed image has clear edge details and good reconstruction effect, while the block effect is effectively suppressed. The framework designed in this paper is superior to similar algorithms in both subjective visual and objective evaluation indexes, which is of great significance for alleviating the incompatibility between traditional information acquisition methods and satellite-borne earth observation missions. [ABSTRACT FROM AUTHOR]

Current image feature extraction methods fail to adapt to the fine features of apple image texture, resulting in image matching errors and degraded image processing accuracy. A multi-view orthogonal image acquisition system was constructed with apples as the research object. The system consists of four industrial cameras placed around the apple at different angles and one camera placed on top. Following the image acquisition through the system, synthetic image pairs—both before and after transformation—were generated as the input dataset. This generation process involved each image being subjected to random transformations. Through learning to extract more distinctive and descriptive features, the deep learning-based keypoint detection method surpasses traditional techniques by broadening the application range and enhancing detection accuracy. Therefore, a lightweight network called ALIKE-APPLE was proposed for surface feature point detection. The baseline model for ALIKE-APPLE is ALIKE, upon which improvements have been made to the image feature encoder and feature aggregation modules. It comprises an Improved Convolutional Attention Module (ICBAM) and a Boosting Resolution Sampling Module (BRSM). The proposed ICBAM replaced max pooling in the original image feature encoder for downsampling. It enhanced the feature fusion capability of the model by utilizing spatial contextual information and learning region associations in the image. The proposed BRSM replaced the bilinear interpolation in the original feature aggregator for upsampling, overcoming the apple side image's geometric distortion and effectively preserving the texture details and edge information. The model size was shrunk by optimizing the number of downsampling operations from the image encoder of the original model. The experimental results showed that the average number of observed keypoints and the average matching accuracy were improved by 166.41% and 37.07%, respectively, compared with the baseline model. The feature detection model of ALIKE-APPLE was found to perform better than the optimal SuperPoint. The feature point distribution of ALIKE-APPLE showed an improvement of 10.29% in average standard deviation (Std), 8.62% in average coefficient of variation (CV), and 156.12% in average feature point density (AFPD). Moreover, the mean matching accuracy (MMA) of ALIKE-APPLE improved by 125.97%. Thus, ALIKE-APPLE boasts a more consistent allocation of feature points and greater precision in matching. [ABSTRACT FROM AUTHOR]

To obtain timely, accurate, and reliable information on wheat yield dynamics. The UAV DJI Wizard 4-multispectral version was utilized to acquire multispectral images of winter wheat during the tasseling, grouting, and ripening periods, and to manually acquire ground yield data. Sixteen vegetation indices were screened by correlation analysis, and eight textural features were extracted from five single bands in three fertility periods. Subsequently, models for estimating winter wheat yield were developed utilizing multiple linear regression (MLR), partial least squares (PLS), BP neural network (BPNN), and random forest regression (RF), respectively. (1) The results indicated a consistent correlation between the two variable types and yield across various fertility periods. This correlation consistently followed a sequence: heading period > filling period > mature stage. (2) The model's accuracy improves significantly when incorporating both texture features and vegetation indices for estimation, surpassing the accuracy achieved through the estimation of a single variable type. (3) Among the various models considered, the partial least squares (PLS) model integrating texture features and vegetation indices exhibited the highest accuracy in estimating winter wheat yield. It achieved a coefficient of determination (R2) of 0.852, a root mean square error (RMSE) of 74.469 kg·hm−2, and a normalized root mean square error (NRMSE) of 7.41%. This study validates the significance of utilizing image texture features along with vegetation indices to enhance the accuracy of models estimating winter wheat yield. It demonstrates that UAV multispectral images can effectively establish a yield estimation model. Combining vegetation indices and texture features results in a more accurate and predictive model compared to using a single index. [ABSTRACT FROM AUTHOR]

Eliminating mixed noise from images is a challenging task because accurately describing the attenuation of noise distribution is difficult. However, most existing algorithms for mixed noise removal solely rely on the local information of the image and neglect the global information, resulting in suboptimal denoising performance when dealing with complex mixed noise. In this paper, we propose a nested UNet based on multi-scale feature extraction (MSNUNet) for mixed noise removal. In MSNUNet, we introduce a U-shaped subnetwork called MSU-Subnet for multi-scale feature extraction. These multi-scale features contain abundant local and global features, aiding the model in estimating noise more accurately and improving its robustness. Furthermore, we introduce a multi-scale feature fusion channel attention module (MSCAM) to effectively aggregate feature information from different scales while preserving intricate image texture details. Our experimental results demonstrate that MSNUNet achieves leading performance in terms of quality metrics and the visual appearance of images. [ABSTRACT FROM AUTHOR]

In the field of single-image super-resolution reconstruction, GAN can obtain the image texture more in line with the human eye. However, during the reconstruction process, it is easy to generate artifacts, false textures, and large deviations in details between the reconstructed image and the Ground Truth. In order to further improve the visual quality, we study the feature correlation between adjacent layers and propose a differential value dense residual network to solve this problem. We first use the deconvolution layer to enlarge the features, then extract the features through the convolution layer, and finally make a difference between the features before being magnified and the features after being extracted so that the difference can better reflect the areas that need attention. In the process of extracting the differential value, using the dense residual connection method for each layer can make the magnified features more complete, so the differential value obtained is more accurate. Next, the joint loss function is introduced to fuse high-frequency information and low-frequency information, which improves the visual effect of the reconstructed image to a certain extent. The experimental results on Set5, Set14, BSD100, and Urban datasets show that our proposed DVDR-SRGAN model is improved in terms of PSNR, SSIM, and LPIPS compared with the Bicubic, SRGAN, ESRGAN, Beby-GAN, and SPSR models. [ABSTRACT FROM AUTHOR]

Marine oil spills are one of the major threats to marine ecological safety, and the rapid identification of oil films is of great significance to the emergency response. Marine radar can provide data for marine oil spill detection; however, to date, it has not been commonly reported. Traditional marine radar oil spill research is mostly based on grayscale segmentation, and its accuracy depends entirely on the selection of the threshold. With the development of algorithm technology, marine radar oil spill extraction has gradually come to focus on artificial intelligence, and the study of oil spills based on machine learning has begun to develop. Based on X-band marine radar images collected from the Dalian 716 incident, this study used image texture features, the BP neural network classifier, and threshold segmentation for oil spill extraction. Firstly, the original image was pre-processed, to eliminate co-channel interference noise. Secondly, texture features were extracted and analyzed by the gray-level co-occurrence matrix (GLCM) and principal component analysis (PCA); then, the BP neural work was used to obtain the effective wave region. Finally, threshold segmentation was performed, to extract the marine oil slicks. The constructed BP neural network could achieve 93.75% classification accuracy, with the oil film remaining intact and the segmentation range being small; the extraction results were almost free of false positive targets, and the actual area of the oil film was calculated to be 42,629.12 m2. The method proposed in this paper can provide a reference for real-time monitoring of oil spill incidents. [ABSTRACT FROM AUTHOR]

Image deblurring attracts research attention in the field of image processing and computer vision. Traditional deblurring methods based on statistical prior largely depend on the selected prior type, which limits their restoring ability. Moreover, the constructed deblurring model is difficult to solve, and the operation is comparatively complicated. Meanwhile, deep learning has become a hotspot in various fields in recent years. End-to-end convolutional neural networks (CNNs) can learn the pixel mapping relationships between degraded images and clear images. In addition, they can also obtain the result of effectively eliminating spatial variable blurring. However, conventional CNNs have some disadvantages in generalization ability and details of the restored image. Therefore, this paper presents an iterative dual CNN called IDC for image deblurring, where the task of image deblurring is divided into two sub-networks: deblurring and detail restoration. The deblurring sub-network adopts a U-Net structure to learn the semantical and structural features of the image, and the detail restoration sub-network utilizes a shallow and wide structure without downsampling, where only the image texture features are extracted. Finally, to obtain the deblurred image, this paper presents a multiscale iterative strategy that effectively improves the robustness and precision of the model. The experimental results showed that the proposed method has an excellent effect of deblurring on a real blurred image dataset and is suitable for various real application scenes. [ABSTRACT FROM AUTHOR]

Proximate analysis, including ash, volatile matter, moisture, fixed carbon, and calorific value, is a fundamental aspect of fuel testing and serves as the primary method for evaluating coal quality, which is critical for the processing and utilization of coal. The traditional analytical methods involve time-consuming and costly combustion processes, particularly when applied to large volumes of coal that need to be sampled in massive batches. Hyperspectral imaging is promising for the rapid and nondestructive determination of coal quality indices. In this study, a fast and nondestructive coal proximate analysis method with combined spectral-spatial features was developed using a hyperspectral imaging system in the 450–2500 nm range. The processed spectra were evaluated using PLSR, with the most effective MSC spectra selected. To reduce the spectral redundancy and improve the accuracy, the SPA, Boruta, iVISSA, and CARS algorithms were adopted to extract the characteristic wavelengths, and 16 prediction models were constructed and optimized based on the PLSR, RF, BPNN, and LSSVR algorithms within the Optuna framework for each quality indicator. For spatial information, the histogram statistics, gray-level covariance matrix, and Gabor filters were employed to extract the texture features within the characteristic wavelengths. The texture feature-based and combined spectral-texture feature-based prediction models were constructed by applying the spectral modeling strategy, respectively. Compared with the models based on spectral or texture features only, the LSSVR models with combined spectral-texture features achieved the highest prediction accuracy in all quality metrics, with R p 2 values of 0.993, 0.989, 0.979, 0.948, and 0.994 for Ash, VM, MC, FC, and CV, respectively. This study provides a technical reference for hyperspectral imaging technology as a new method for the rapid, nondestructive proximate analysis and quality assessment of coal. [ABSTRACT FROM AUTHOR]

Single-image super-resolution (SISR) seeks to elucidate the mapping relationships between low-resolution and high-resolution images. However, high-performance network models often entail a significant number of parameters and computations, presenting limitations in practical applications. Therefore, prioritizing a light weight and efficiency becomes crucial when applying image super-resolution (SR) to real-world scenarios. We propose a straightforward and efficient method, the Multi-Branch Feature Extraction Residual Network (MFERN), to tackle lightweight image SR through the fusion of multi-information self-calibration and multi-attention information. Specifically, we have devised a Multi-Branch Residual Feature Fusion Module (MRFFM) that leverages a multi-branch residual structure to succinctly and effectively fuse multiple pieces of information. Within the MRFFM, we have designed the Multi-Scale Attention Feature Fusion Block (MAFFB) to adeptly extract features via convolution and self-calibration attention operations. Furthermore, we introduce a Dual Feature Calibration Block (DFCB) to dynamically fuse feature information using dynamic weight values derived from the upper and lower branches. Additionally, to overcome the limitation of convolution in solely extracting local information, we incorporate a Transformer module to effectively integrate global information. The experimental results demonstrate that our MFERN exhibits outstanding performance in terms of model parameters and overall performance. [ABSTRACT FROM AUTHOR]

Entomological radar is one of the most effective tools for monitoring insect migration, capable of detecting migratory insects concentrated in layers and facilitating the analysis of insect migration behavior. However, traditional entomological radar, with its low resolution, can only provide a rough observation of layer concentrations. The advent of High-Resolution Phased Array Radar (HPAR) has transformed this situation. With its high range resolution and high data update rate, HPAR can generate detailed concentration spatiotemporal distribution heatmaps. This technology facilitates the detection of changes in insect concentrations across different time periods and altitudes, thereby enabling the observation of large-scale take-off, landing, and layering phenomena. However, the lack of effective techniques for extracting insect concentration data of different phenomena from these heatmaps significantly limits detailed analyses of insect migration patterns. This paper is the first to apply instance segmentation technology to the extraction of insect data, proposing a method for segmenting and extracting insect concentration data from spatiotemporal distribution heatmaps at different phenomena. To address the characteristics of concentrations in spatiotemporal distributions, we developed the Heatmap Feature Fusion Network (HFF-Net). In HFF-Net, we incorporate the Global Context (GC) module to enhance feature extraction of concentration distributions, utilize the Atrous Spatial Pyramid Pooling with Depthwise Separable Convolution (SASPP) module to extend the receptive field for understanding various spatiotemporal distributions of concentrations, and refine segmentation masks with the Deformable Convolution Mask Fusion (DCMF) module to enhance segmentation detail. Experimental results show that our proposed network can effectively segment concentrations of different phenomena from heatmaps, providing technical support for detailed and systematic studies of insect migration behavior. [ABSTRACT FROM AUTHOR]

Camellia oleifera is a crop of high economic value, yet it is particularly susceptible to various diseases and pests that significantly reduce its yield and quality. Consequently, the precise segmentation and classification of diseased Camellia leaves are vital for managing pests and diseases effectively. Deep learning exhibits significant advantages in the segmentation of plant diseases and pests, particularly in complex image processing and automated feature extraction. However, when employing single-modal models to segment Camellia oleifera diseases, three critical challenges arise: (A) lesions may closely resemble the colors of the complex background; (B) small sections of diseased leaves overlap; (C) the presence of multiple diseases on a single leaf. These factors considerably hinder segmentation accuracy. A novel multimodal model, CNN–Transformer Dual U-shaped Network (CTDUNet), based on a CNN–Transformer architecture, has been proposed to integrate image and text information. This model first utilizes text data to address the shortcomings of single-modal image features, enhancing its ability to distinguish lesions from environmental characteristics, even under conditions where they closely resemble one another. Additionally, we introduce Coordinate Space Attention (CSA), which focuses on the positional relationships between targets, thereby improving the segmentation of overlapping leaf edges. Furthermore, cross-attention (CA) is employed to align image and text features effectively, preserving local information and enhancing the perception and differentiation of various diseases. The CTDUNet model was evaluated on a self-made multimodal dataset compared against several models, including DeeplabV3+, UNet, PSPNet, Segformer, HrNet, and Language meets Vision Transformer (LViT). The experimental results demonstrate that CTDUNet achieved an mean Intersection over Union (mIoU) of 86.14%, surpassing both multimodal models and the best single-modal model by 3.91% and 5.84%, respectively. Additionally, CTDUNet exhibits high balance in the multi-class segmentation of Camellia oleifera diseases and pests. These results indicate the successful application of fused image and text multimodal information in the segmentation of Camellia disease, achieving outstanding performance. [ABSTRACT FROM AUTHOR]

Industrial anomaly detection is constrained by the scarcity of anomaly samples, limiting the applicability of supervised learning methods. Many studies have focused on anomaly detection by generating anomaly images and adopting self-supervised learning approaches. Leveraging pre-trained networks on ImageNet has been explored to assist in this training process. However, achieving accurate anomaly detection remains time-consuming due to the network's depth and parameter count not being reduced. In this paper, we propose a self-supervised learning method based on Feature Enhancement Patch Distribution Modeling (FEPDM), which generates simulated anomalies. Unlike direct training on the original feature extraction network, our approach utilizes a pre-trained network to extract multi-scale features. By aggregating these multi-scale features, we are able to train at the feature level, thereby adapting more efficiently to various network structures and reducing domain bias with respect to natural image classification. Additionally, it significantly reduces the number of parameters in the training process. Introducing this approach not only enhances the model's generalization ability but also significantly improves the efficiency of anomaly detection. The method was evaluated on MVTec AD and BTAD datasets, and (image-level, pixel-level) AUROC scores of (95.7%, 96.2%), (93.4%, 97.6%) were obtained, respectively. The experimental results have convincingly demonstrated the efficacy of our method in tackling the scarcity of abnormal samples in industrial scenarios, while simultaneously highlighting its broad generalizability. [ABSTRACT FROM AUTHOR]

The coastal zone is abundant in natural resources but has become increasingly fragile in recent years due to climate change and extensive, improper exploitation. Accurate land use and land cover (LULC) mapping of coastal zones using remotely sensed data is crucial for monitoring environmental changes. Traditional classification methods based on statistical learning require significant spectral differences between ground objects. However, state-of-the-art end-to-end deep learning methods can extract advanced features from remotely sensed data. In this study, we employed ResNet50 as the feature extraction network within the U-Net architecture to achieve accurate classification of coastal areas and assess the model's performance. Experiments were conducted using Gaofen-2 (GF-2) high-resolution remote sensing data from Shuangyue Bay, a typical coastal area in Guangdong Province. We compared the classification results with those obtained from two popular deep learning models, SegNet and DeepLab v3+, as well as two advanced statistical learning models, Support Vector Machine (SVM) and Random Forest (RF). Additionally, this study further explored the significance of Gray Level Co-occurrence Matrix (GLCM) texture features, Histogram Contrast (HC) features, and Normalized Difference Vegetation Index (NDVI) features in the classification of coastal areas. The research findings indicated that under complex ground conditions, the U-Net model achieved the highest overall accuracy of 86.32% using only spectral channels from GF-2 remotely sensed data. When incorporating multiple features, including spectrum, texture, contrast, and vegetation index, the classification accuracy of the U-Net algorithm significantly improved to 93.65%. The major contributions of this study are twofold: (1) it demonstrates the advantages of deep learning approaches, particularly the U-Net model, for LULC classification in coastal zones using high-resolution remote sensing images, and (2) it analyzes the contributions of spectral and spatial features of GF-2 data for different land cover types through a spectral and spatial combination method. [ABSTRACT FROM AUTHOR]

The complementary combination of emphasizing target objects in infrared images and rich texture details in visible images can effectively enhance the information entropy of fused images, thereby providing substantial assistance for downstream composite high-level vision tasks, such as nighttime vehicle intelligent driving. However, mainstream fusion algorithms lack specific research on the contradiction between the low information entropy and high pixel intensity of visible images under harsh light nighttime road environments. As a result, fusion algorithms that perform well in normal conditions can only produce low information entropy fusion images similar to the information distribution of visible images under harsh light interference. In response to these problems, we designed an image fusion network resilient to harsh light environment interference, incorporating entropy and information theory principles to enhance robustness and information retention. Specifically, an edge feature extraction module was designed to extract key edge features of salient targets to optimize fusion information entropy. Additionally, a harsh light environment aware (HLEA) module was proposed to avoid the decrease in fusion image quality caused by the contradiction between low information entropy and high pixel intensity based on the information distribution characteristics of harsh light visible images. Finally, an edge-guided hierarchical fusion (EGHF) module was designed to achieve robust feature fusion, minimizing irrelevant noise entropy and maximizing useful information entropy. Extensive experiments demonstrate that, compared to other advanced algorithms, the method proposed fusion results contain more useful information and have significant advantages in high-level vision tasks under harsh nighttime lighting conditions. [ABSTRACT FROM AUTHOR]

Poplar (Populus L.) anthracnose is an infectious disease that seriously affects the growth and yields of poplar trees, and large-scale poplar infections have led to huge economic losses in the Chinese poplar industry. To efficiently and accurately detect poplar anthracnose for improved prevention and control, this study collected hyperspectral data from the leaves of four types of poplar trees, namely healthy trees and those with black spot disease, early-stage anthracnose, and late-stage anthracnose, and constructed a poplar anthracnose detection model based on machine learning and deep learning. We then comprehensively analyzed poplar anthracnose using advanced hyperspectral-based plant disease detection methodologies. Our research focused on establishing a detection model for poplar anthracnose based on small samples, employing the Design of Experiments (DoE)-based entropy weight method to obtain the best preprocessing combination to improve the detection model's overall performance. We also analyzed the spectral characteristics of poplar anthracnose by comparing typical feature extraction methods (principal component analysis (PCA), variable combination population analysis (VCPA), and the successive projection algorithm (SPA)) with the vegetation index (VI) method (spectral disease indices (SDIs)) for data dimensionality reduction. The results showed notable improvements in the SDI-based model, which achieved 89.86% accuracy. However, this was inferior to the model based on typical feature extraction methods. Nevertheless, it achieved 100% accuracy for early-stage anthracnose and black spot disease in a controlled environment respectively. We conclude that the SDI-based model is suitable for low-cost detection tasks and is the best poplar anthracnose detection model. These findings contribute to the timely detection of poplar growth and will greatly facilitate the forestry sector's development. [ABSTRACT FROM AUTHOR]

The question "How does it work" has motivated many scientists. Through the study of natural phenomena and behaviors, many intelligence algorithms have been proposed to solve various optimization problems. This paper aims to offer an informative guide for researchers who are interested in tackling optimization problems with intelligence algorithms. First, a special neural network was comprehensively discussed, and it was called a zeroing neural network (ZNN). It is especially intended for solving time-varying optimization problems, including origin, basic principles, operation mechanism, model variants, and applications. This paper presents a new classification method based on the performance index of ZNNs. Then, two classic bio-inspired algorithms, a genetic algorithm and a particle swarm algorithm, are outlined as representatives, including their origin, design process, basic principles, and applications. Finally, to emphasize the applicability of intelligence algorithms, three practical domains are introduced, including gene feature extraction, intelligence communication, and the image process. [ABSTRACT FROM AUTHOR]

The surface quality of milled blade-root grooves in industrial turbine blades significantly influences their mechanical properties. The surface texture reveals the interaction between the tool and the workpiece during the machining process, which plays a key role in determining the surface quality. In addition, there is a significant correlation between acoustic vibration signals and surface texture features. However, current research on surface quality is still relatively limited, and most considers only a single signal. In this paper, 160 sets of industrial field data were collected by multiple sensors to study the surface quality of a blade-root groove. A surface texture feature prediction method based on acoustic vibration signal fusion is proposed to evaluate the surface quality. Fast Fourier transform (FFT) is used to process the signal, and the clean and smooth features are extracted by combining wavelet denoising and multivariate smoothing denoising. At the same time, based on the gray-level co-occurrence matrix, the surface texture image features of different angles of the blade-root groove are extracted to describe the texture features. The fused acoustic vibration signal features are input, and the texture features are output to establish a texture feature prediction model. After predicting the texture features, the surface quality is evaluated by setting a threshold value. The threshold is selected based on all sample data, and the final judgment accuracy is 90%. [ABSTRACT FROM AUTHOR]

The accurate extraction of agricultural parcels from remote sensing images is crucial for advanced agricultural management and monitoring systems. Existing methods primarily emphasize regional accuracy over boundary quality, often resulting in fragmented outputs due to uniform crop types, diverse agricultural practices, and environmental variations. To address these issues, this paper proposes DSTBA-Net, an end-to-end encoder–decoder architecture. Initially, we introduce a Dual-Stream Feature Extraction (DSFE) mechanism within the encoder, which consists of Residual Blocks and Boundary Feature Guidance (BFG) to separately process image and boundary data. The extracted features are then fused in the Global Feature Fusion Module (GFFM), utilizing Transformer technology to further integrate global and detailed information. In the decoder, we employ Feature Compensation Recovery (FCR) to restore critical information lost during the encoding process. Additionally, the network is optimized using a boundary-aware weighted loss strategy. DSTBA-Net aims to achieve high precision in agricultural parcel segmentation and accurate boundary extraction. To evaluate the model's effectiveness, we conducted experiments on agricultural parcel extraction in Denmark (Europe) and Shandong (Asia). Both quantitative and qualitative analyses show that DSTBA-Net outperforms comparative methods, offering significant advantages in agricultural parcel extraction. [ABSTRACT FROM AUTHOR]

Image super-resolution (SR) technology can improve the resolution of images and provide clearer and more reliable remote sensing images of high quality to better serve the subsequent applications. However, when reconstructing high-frequency feature areas of remote sensing images, existing SR reconstruction methods are prone to artifacts that affect visual effects and make it difficult to generate real texture details. In order to address this issue, a texture-detail-enhanced dense generative adversarial network (TDEGAN) for remote sensing image SR is presented. The generator uses multi-level dense connections, residual connections, and Shuffle attention (SA) to improve the feature extraction ability. A PatchGAN-style discrimination network is designed to effectively perform local discrimination and helps the network generate rich, detailed features. To reduce the impact of artifacts, we introduce an artifact loss function, which is combined with the exponential moving average (EMA) technique to distinguish the artifacts generated from the actual texture details through local statistics, which can help the network reduce artifacts and generate more realistic texture details. Experiments show that TDEGAN can better restore the texture details of remote sensing images and achieves certain advantages in terms of evaluation indicators and visualization. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous systems, allowing them to both navigate through and create maps of unfamiliar surroundings. Traditional Visual SLAM, also commonly known as VSLAM, relies on frame-based cameras and structured processing pipelines, which face challenges in dynamic or low-light environments. However, recent advancements in event camera technology and neuromorphic processing offer promising opportunities to overcome these limitations. Event cameras inspired by biological vision systems capture the scenes asynchronously, consuming minimal power but with higher temporal resolution. Neuromorphic processors, which are designed to mimic the parallel processing capabilities of the human brain, offer efficient computation for real-time data processing of event-based data streams. This paper provides a comprehensive overview of recent research efforts in integrating event cameras and neuromorphic processors into VSLAM systems. It discusses the principles behind event cameras and neuromorphic processors, highlighting their advantages over traditional sensing and processing methods. Furthermore, an in-depth survey was conducted on state-of-the-art approaches in event-based SLAM, including feature extraction, motion estimation, and map reconstruction techniques. Additionally, the integration of event cameras with neuromorphic processors, focusing on their synergistic benefits in terms of energy efficiency, robustness, and real-time performance, was explored. The paper also discusses the challenges and open research questions in this emerging field, such as sensor calibration, data fusion, and algorithmic development. Finally, the potential applications and future directions for event-based SLAM systems are outlined, ranging from robotics and autonomous vehicles to augmented reality. [ABSTRACT FROM AUTHOR]

Due to the widespread use of mulching film in cotton planting in China, residual mulching film mixed with machine-picked cotton poses a significant hazard to cotton processing. Detecting residual mulching film in seed cotton has become particularly challenging due to the film's semi-transparent nature. This study constructed an imaging system combining an area array camera and a line scan camera. A detection scheme was proposed that utilized features from both image types. To simulate online detection, samples were placed on a conveyor belt moving at 0.2 m/s, with line lasers at a wavelength of 650 nm as light sources. For area array images, feature extraction was performed to establish a partial least squares discriminant analysis (PLS-DA) model. For line scan images, texture feature analysis was used to build a support vector machine (SVM) classification model. Subsequently, image features from both cameras were merged to construct an SVM model. Experimental results indicated that detection methods based on area array and line scan images had accuracies of 75% and 79%, respectively, while the feature fusion method achieved an accuracy of 83%. This study demonstrated that the proposed method could effectively improve the accuracy of residual mulching film detection in seed cotton, providing a basis for reducing residual mulching film content during processing. [ABSTRACT FROM AUTHOR]

The fusion of multi-modal medical images has great significance for comprehensive diagnosis and treatment. However, the large differences between the various modalities of medical images make multi-modal medical image fusion a great challenge. This paper proposes a novel multi-scale fusion network based on multi-dimensional dynamic convolution and residual hybrid transformer, which has better capability for feature extraction and context modeling and improves the fusion performance. Specifically, the proposed network exploits multi-dimensional dynamic convolution that introduces four attention mechanisms corresponding to four different dimensions of the convolutional kernel to extract more detailed information. Meanwhile, a residual hybrid transformer is designed, which activates more pixels to participate in the fusion process by channel attention, window attention, and overlapping cross attention, thereby strengthening the long-range dependence between different modes and enhancing the connection of global context information. A loss function, including perceptual loss and structural similarity loss, is designed, where the former enhances the visual reality and perceptual details of the fused image, and the latter enables the model to learn structural textures. The whole network adopts a multi-scale architecture and uses an unsupervised end-to-end method to realize multi-modal image fusion. Finally, our method is tested qualitatively and quantitatively on mainstream datasets. The fusion results indicate that our method achieves high scores in most quantitative indicators and satisfactory performance in visual qualitative analysis. [ABSTRACT FROM AUTHOR]

Due to the extremely irregular nonlinear degradation of images obtained in real underwater environments, it is difficult for existing underwater image enhancement methods to stably restore degraded underwater images, thus making it challenging to improve the efficiency of marine work. We propose a hybrid algorithm model for underwater image color restoration and texture enhancement, termed HA-Net. First, we introduce a dynamic color correction algorithm based on depth estimation to restore degraded images and mitigate color attenuation in underwater images by calculating the depth of targets and backgrounds. Then, we propose a multi-scale U-Net to enhance the network's feature extraction capability and introduce a parallel attention module to capture image spatial information, thereby improving the model's accuracy in recognizing deep semantics such as fine texture. Finally, we propose a global information compensation algorithm to enhance the output image's integrity and boost the network's learning ability. Experimental results on synthetic standard data sets and real data demonstrate that our method produces images with clear texture and bright colors, outperforming other algorithms in both subjective and objective evaluations, making it more suitable for real marine environments. [ABSTRACT FROM AUTHOR]

Remote sensing images usually contain abundant targets and complex information distributions. Consequently, networks are required to model both global and local information in the super-resolution (SR) reconstruction of remote sensing images. The existing SR reconstruction algorithms generally focus on only local or global features, neglecting effective feedback for reconstruction errors. Therefore, a Global Residual Multi-attention Fusion Back-projection Network (SRBPSwin) is introduced by combining the back-projection mechanism with the Swin Transformer. We incorporate a concatenated Channel and Spatial Attention Block (CSAB) into the Swin Transformer Block (STB) to design a Multi-attention Hybrid Swin Transformer Block (MAHSTB). SRBPSwin develops dense back-projection units to provide bidirectional feedback for reconstruction errors, enhancing the network's feature extraction capabilities and improving reconstruction performance. SRBPSwin consists of the following four main stages: shallow feature extraction, shallow feature refinement, dense back projection, and image reconstruction. Firstly, for the input low-resolution (LR) image, shallow features are extracted and refined through the shallow feature extraction and shallow feature refinement stages. Secondly, multiple up-projection and down-projection units are designed to alternately process features between high-resolution (HR) and LR spaces, obtaining more accurate and detailed feature representations. Finally, global residual connections are utilized to transfer shallow features during the image reconstruction stage. We propose a perceptual loss function based on the Swin Transformer to enhance the detail of the reconstructed image. Extensive experiments demonstrate the significant reconstruction advantages of SRBPSwin in quantitative evaluation and visual quality. [ABSTRACT FROM AUTHOR]

Imaging hyperspectral technology is becoming popular in agriculture to provide detailed information on crop growth. In this work, we propose an estimation of rapeseed pod's water content model and identification of maturity levels (green, yellow, and full) model by using this technology. Four types of hyperspectral features are extracted—color, texture, spectral three-edge parameters, and spectral indices. By integrating these features, satisfactory results are achieved: the optimal feature combination is from spectral indices and three-edge parameters, with low RRMSE and RE for yellow maturity. Incorporating spectral indices significantly improved the pod's water content estimation, reducing RRMSE by up to 43.30% and 30.11% in the green and full maturity stages. Random forest and support vector machine with kernel method (SVM-KM) algorithms outperformed other statistical models, with SVM-KM achieving up to 96.90% accuracy in identifying maturity levels. These findings provide valuable insights for managing rapeseed production during the pod stage. [ABSTRACT FROM AUTHOR]

Timely and accurate estimation of Above-Ground-Biomass (AGB) in cotton is essential for precise production monitoring. The study was conducted in Shaya County, Aksu Region, Xinjiang, China. It employed an unmanned aerial vehicle (UAV) as a low-altitude monitoring platform to capture multispectral images of the cotton canopy. Subsequently, spectral features and textural features were extracted, and feature selection was conducted using Pearson's correlation (P), Principal Component Analysis (PCA), Multivariate Stepwise Regression (MSR), and the ReliefF algorithm (RfF), combined with the machine learning algorithm to construct an estimation model of cotton AGB. The results indicate a high consistency between the mean (MEA) and the corresponding spectral bands in textural features with the AGB correlation. Moreover, spectral and textural feature fusion proved to be more stable than models utilizing single spectral features or textural features alone. Both the RfF algorithm and ANN model demonstrated optimization effects on features, and their combination effectively reduced the data redundancy while improving the model performance. The RfF-ANN-AGB model constructed based on the spectral and textural features fusion worked better, and using the features SIPI2, RESR, G_COR, and RE_DIS, exhibited the best performance, achieving a test sets R2 of 0.86, RMSE of 0.23 kg·m−2, MAE of 0.16 kg·m−2, and nRMSE of 0.39. The findings offer a comprehensive modeling strategy for the precise and rapid estimation of cotton AGB. [ABSTRACT FROM AUTHOR]

To create a radiogenomics map and evaluate the correlation between molecular and imaging phenotypes in localized prostate cancer (PCa), using radical prostatectomy histopathology as a reference standard. Radiomic features were extracted from T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) images of clinically localized PCa patients (n = 15) across different Gleason score-based risk categories. DNA extraction was performed on formalin-fixed, paraffin-embedded (FFPE) samples. Gene expression analysis of androgen receptor expression, apoptosis, and hypoxia was conducted using the Chromosome Analysis Suite (ChAS) application and OSCHIP files. The relationship between gene expression alterations and textural features was assessed using Pearson's correlation analysis. Receiver operating characteristic (ROC) analysis was utilized to evaluate the predictive accuracy of the model. A significant correlation was observed between radiomic texture features and copy number variation (CNV) of genes associated with apoptosis, hypoxia, and androgen receptor (p-value ≤ 0.05). The identified radiomic features, including Sum Entropy ADC, Inverse Difference ADC, Sum Variance T2WI, Entropy T2WI, Difference Variance T2WI, and Angular Secondary Moment T2WI, exhibited potential for predicting cancer grade and biological processes such as apoptosis and hypoxia. Incorporating radiomics and genomics into a prediction model significantly improved the prediction of prostate cancer grade (clinically significant prostate cancer), yielding an AUC of 0.95. Radiomic texture features significantly correlate with genotypes for apoptosis, hypoxia, and androgen receptor expression in localised prostate cancer. Integration of these into the prediction model improved prediction accuracy of clinically significant prostate cancer. [ABSTRACT FROM AUTHOR]

Underwater images suffer from low contrast and color distortion. In order to improve the quality of underwater images and reduce storage and computational resources, this paper proposes a lightweight model Rep-UWnet to enhance underwater images. The model consists of a fully connected convolutional network and three densely connected RepConv blocks in series, with the input images connected to the output of each block with a Skip connection. First, the original underwater image is subjected to feature extraction by the SimSPPF module and is processed through feature summation with the original one to be produced as the input image. Then, the first convolutional layer with a kernel size of 3 × 3, generates 64 feature maps, and the multi-scale hybrid convolutional attention module enhances the useful features by reweighting the features of different channels. Second, three RepConv blocks are connected to reduce the number of parameters in extracting features and increase the test speed. Finally, a convolutional layer with 3 kernels generates enhanced underwater images. Our method reduces the number of parameters from 2.7 M to 0.45 M (around 83% reduction) but outperforms state-of-the-art algorithms by extensive experiments. Furthermore, we demonstrate our Rep-UWnet effectively improves high-level vision tasks like edge detection and single image depth estimation. This method not only surpasses the contrast method in objective quality, but also significantly improves the contrast, colorimetry, and clarity of underwater images in subjective quality. [ABSTRACT FROM AUTHOR]

This paper presents a computer vision-based standalone decision-aid landing system for light fixed-wing aircraft, aiming to enhance safety during emergency landings. Current landing assistance systems in airports, such as Instrument Landing Systems (ILSs) and Precision Approach Path Indicators (PAPIs), often rely on costly and location-specific ground equipment, limiting their utility for low-payload light aircraft. Especially in emergency conditions, the pilot may be forced to land on an arbitrary runway where the road flatness and glide angle cannot be ensured. To address these issues, a stereo vision-based auxiliary landing system is proposed, which is capable of estimating an appropriate glide slope based on the terrain, to assist pilots in safe landing decision-making. Moreover, in real-world scenarios, challenges with visual-based methods arise when attempting emergency landings on complex terrains with diverse objects, such as roads and buildings. This study solves this problem by employing the Gaussian Mixture Model (GMM) to segment the color image and extract ground points, while the iterative weighted plane fitting (IWPF) algorithm is introduced to mitigate the interference of outlier feature points, reaching a highly robust plane normal estimation. With the aid of the proposed system, the pilot is able to evaluate the landing glide angle/speed with respect to the uneven terrain. Simulation results demonstrate that the proposed system can successfully achieve landing guidance in unknown environments by providing glide angle estimations with an average error of less than 1 degree. [ABSTRACT FROM AUTHOR]

Simple Summary: In response to the quantitative demand for the practical deployment of applications and the need to enhance the detection accuracy of wildlife in complex field environments in southwest China and neighboring regions, we have refined the wildlife detection algorithm based on re-parameterized convolution. This refinement is specifically targeted at addressing the challenges posed by the low quality of wildlife images captured by camera traps and the limitations of traditional object detection algorithms in feature extraction capability. To address these issues, we have introduced a series of improvement schemes. As a result of these enhancements, there has been a noteworthy improvement in both the accuracy of wildlife detection and the speed of model inference. This advancement offers a convenient and efficient method for the preliminary detection in the context of automated wildlife monitoring. To autonomously detect wildlife images captured by camera traps on a platform with limited resources and address challenges such as filtering out photos without optimal objects, as well as classifying and localizing species in photos with objects, we introduce a specialized wildlife object detector tailored for camera traps. This detector is developed using a dataset acquired by the Saola Working Group (SWG) through camera traps deployed in Vietnam and Laos. Utilizing the YOLOv6-N object detection algorithm as its foundation, the detector is enhanced by a tailored optimizer for improved model performance. We deliberately introduce asymmetric convolutional branches to enhance the feature characterization capability of the Backbone network. Additionally, we streamline the Neck and use CIoU loss to improve detection performance. For quantitative deployment, we refine the RepOptimizer to train a pure VGG-style network. Experimental results demonstrate that our proposed method empowers the model to achieve an 88.3% detection accuracy on the wildlife dataset in this paper. This accuracy is 3.1% higher than YOLOv6-N, and surpasses YOLOv7-T and YOLOv8-N by 5.5% and 2.8%, respectively. The model consistently maintains its detection performance even after quantization to the INT8 precision, achieving an inference speed of only 6.15 ms for a single image on the NVIDIA Jetson Xavier NX device. The improvements we introduce excel in tasks related to wildlife image recognition and object localization captured by camera traps, providing practical solutions to enhance wildlife monitoring and facilitate efficient data acquisition. Our current work represents a significant stride toward a fully automated animal observation system in real-time in-field applications. [ABSTRACT FROM AUTHOR]

Many new reconstruction techniques have been deployed to allow low-dose CT examinations. Such reconstruction techniques exhibit nonlinear properties, which strengthen the need for a task-based measure of image quality. The Hotelling observer (HO) is the optimal linear observer and provides a lower bound of the Bayesian ideal observer detection performance. However, its computational complexity impedes its widespread practical usage. To address this issue, we proposed a self-supervised learning (SSL)-based model observer to provide accurate estimates of HO performance in very low-dose chest CT images. Our approach involved a two-stage model combining a convolutional denoising auto-encoder (CDAE) for feature extraction and dimensionality reduction and a support vector machine for classification. To evaluate this approach, we conducted signal detection tasks employing chest CT images with different noise structures generated by computer-based simulations. We compared this approach with two supervised learning-based methods: a single-layer neural network (SLNN) and a convolutional neural network (CNN). The results showed that the CDAE-based model was able to achieve similar detection performance to the HO. In addition, it outperformed both SLNN and CNN when a reduced number of training images was considered. The proposed approach holds promise for optimizing low-dose CT protocols across scanner platforms. [ABSTRACT FROM AUTHOR]

Medium- to high-resolution imagery is indispensable for various applications. Combining images from Landsat 8 and Sentinel-2 can improve the accuracy of observing dynamic changes on the Earth's surface. Many researchers use Sentinel-2 10 m resolution data in conjunction with Landsat 8 30 m resolution data to generate 10 m resolution data series. However, current fusion techniques have some algorithmic weaknesses, such as simple processing of coarse or fine images, which fail to extract image features to the fullest extent, especially in rapidly changing land cover areas. Facing the aforementioned limitations, we proposed a multiscale and attention mechanism-based residual spatiotemporal fusion network (MARSTFN) that utilizes Sentinel-2 10 m resolution data and Landsat 8 15 m resolution data as auxiliary data to upgrade Landsat 8 30 m resolution data to 10 m resolution. In this network, we utilized multiscale and attention mechanisms to extract features from coarse and fine images separately. Subsequently, the features outputted from all input branches are combined and further feature information is extracted through residual networks and skip connections. Finally, the features obtained from the residual network are merged with the feature information of the coarsely processed images from the multiscale mechanism to generate accurate prediction images. To assess the efficacy of our model, we compared it with existing models on two datasets. Results demonstrated that our fusion model outperformed baseline methods across various evaluation indicators, highlighting its ability to integrate Sentinel-2 and Landsat 8 data to produce 10 m resolution data. [ABSTRACT FROM AUTHOR]

The unique imaging modality of synthetic aperture radar (SAR) has posed significant challenges for object detection, making it more complex to acquire and interpret than optical images. Recently, numerous studies have proposed cross-domain adaptive methods based on convolutional neural networks (CNNs) to promote SAR object detection using optical data. However, existing cross-domain methods focus on image features, lack improvement on input data, and ignore the valuable supervision provided by few labeled SAR images. Therefore, we propose a semi-supervised cross-domain object detection framework that uses optical data and few SAR data to achieve knowledge transfer for SAR object detection. Our method focuses on the data processing aspects to gradually reduce the domain shift at the image, instance, and feature levels. First, we propose a data augmentation method of image mixing and instance swapping to generate a mixed domain that is more similar to the SAR domain. This method fully utilizes few SAR annotation information to reduce domain shift at image and instance levels. Second, at the feature level, we propose an adaptive optimization strategy to filter out mixed domain samples that significantly deviate from the SAR feature distribution to train feature extractor. In addition, we employ Vision Transformer (ViT) as feature extractor to handle the global feature extraction of mixed images. We propose a detection head based on normalized Wasserstein distance (NWD) to enhance objects with smaller effective regions in SAR images. The effectiveness of our proposed method is evaluated on public SAR ship and oil tank datasets. [ABSTRACT FROM AUTHOR]

In recent years, deep convolutional neural networks with multi-scale features have been widely used in image super-resolution reconstruction (ISR), and the quality of the generated images has been significantly improved compared with traditional methods. However, in current image super-resolution network algorithms, these methods need to be further explored in terms of the effective fusion of multi-scale features and cross-domain application of attention mechanisms. To address these issues, we propose a novel multi-scale cross-attention fusion network (MCFN), which optimizes the feature extraction and fusion process in structural design and modular innovation. In order to make better use of the attention mechanism, we propose a Pyramid Multi-scale Module (PMM) to extract multi-scale information by cascading. This PMM is introduced in MCFN and is mainly constructed by multiple multi-scale cross-attention modules (MTMs). To fuse the feature information of PMMs efficiently in both channel and spatial dimensions, we propose the cross-attention fusion module (CFM). In addition, an improved integrated attention enhancement module (IAEM) is inserted at the network's end to enhance the correlation of high-frequency feature information between layers. Experimental results show that the algorithm significantly improves the reconstructed images' edge information and texture details, and the benchmark dataset's performance evaluation shows comparable performance to current state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Efficient flotation beneficiation heavily relies on accurate flotation condition recognition based on monitored froth video. However, the recognition accuracy is hindered by limitations of extracting temporal features from froth videos and establishing correlations between complex multi-modal high-order data. To address the difficulties of inadequate temporal feature extraction, inaccurate online condition detection, and inefficient flotation process operation, this paper proposes a novel flotation condition recognition method named the multi-modal temporal hypergraph neural network (MTHGNN) to extract and fuse multi-modal temporal features. To extract abundant dynamic texture features from froth images, the MTHGNN employs an enhanced version of the local binary pattern algorithm from three orthogonal planes (LBP-TOP) and incorporates additional features from the three-dimensional space as supplements. Furthermore, a novel multi-view temporal feature aggregation network (MVResNet) is introduced to extract temporal aggregation features from the froth image sequence. By constructing a temporal multi-modal hypergraph neural network, we encode complex high-order temporal features, establish robust associations between data structures, and flexibly model the features of froth image sequence, thus enabling accurate flotation condition identification through the fusion of multi-modal temporal features. The experimental results validate the effectiveness of the proposed method for flotation condition recognition, providing a foundation for optimizing flotation operations. [ABSTRACT FROM AUTHOR]

Steel surfaces often display intricate texture patterns that can resemble defects, posing a challenge in accurately identifying actual defects. Therefore, it is crucial to develop a highly robust defect detection model. This study proposes a defect detection method for steel infrared images based on a Regularized YOLO framework. Firstly, the Coordinate Attention (CA) is embedded within the C2F framework, utilizing a lightweight attention module to enhance the feature extraction capability of the backbone network. Secondly, the neck part design incorporates the Bi-directional Feature Pyramid Network (BiFPN) for weighted fusion of multi-scale feature maps. This creates a model called BiFPN-Concat, which enhances feature fusion capability. Finally, the loss function of the model is regularized to improve the generalization performance of the model. The experimental results indicate that the model has only 3.03 M parameters, yet achieves a mAP@0.5 of 80.77% on the NEU-DET dataset and 99.38% on the ECTI dataset. This represents an improvement of 2.3% and 1.6% over the baseline model, respectively. This method is well-suited for industrial detection applications involving non-destructive testing of steel using infrared imagery. [ABSTRACT FROM AUTHOR]

Artifacts are the main cause of degradation in CT image quality and diagnostic accuracy. Because of the complex texture of CT images, it is a challenging task to automatically detect artifacts from limited image samples. Recently, graph convolutional networks (GCNs) have achieved great success and shown promising results in medical imaging due to their powerful learning ability. However, GCNs do not take the attention mechanism into consideration. To overcome their limitations, we propose a novel Regional–Temporal Graph Attention Network for motion artifact detection from computed tomography images (RT-GAT). In this paper, head CT images are viewed as a heterogeneous graph by taking regional and temporal information into consideration, and the graph attention network is utilized to extract the features of the constructed graph. Then, the feature vector is input into the classifier to detect the motion artifacts. The experimental results demonstrate that our proposed RT-GAT method outperforms the state-of-the-art methods on a real-world CT dataset. [ABSTRACT FROM AUTHOR]

Deep learning is an important research topic in the field of image super-resolution. Problematically, the performance of existing hyperspectral image super-resolution networks is limited by feature learning for hyperspectral images. Nevertheless, the current algorithms exhibit some limitations in extracting diverse features. In this paper, we address limitations to existing hyperspectral image super-resolution networks, focusing on feature learning challenges. We introduce the Channel-Attention-Based Spatial–Spectral Feature Extraction network (CSSFENet) to enhance hyperspectral image feature diversity and optimize network loss functions. Our contributions include: (a) a convolutional neural network super-resolution algorithm incorporating diverse feature extraction to enhance the network's diversity feature learning by elevating the matrix rank, (b) a three-dimensional (3D) feature extraction convolution module, the Channel-Attention-Based Spatial–Spectral Feature Extraction Module (CSSFEM), to boost the network's performance in both the spatial and spectral domains, (c) a feature diversity loss function designed based on the image matrix's singular value to maximize element independence, and (d) a spatial–spectral gradient loss function introduced based on space and spectrum gradient values to enhance the reconstructed image's spatial–spectral smoothness. In contrast to existing hyperspectral super-resolution algorithms, we used four evaluation indexes, PSNR, mPSNR, SSIM, and SAM, and our method showed superiority during testing with three common hyperspectral datasets. [ABSTRACT FROM AUTHOR]

Identifying patient posture while they are lying in bed is an important task in medical applications such as monitoring a patient after a surgical intervention, sleep supervision to identify behavioral and physiological markers, or for bedsore prevention. An acceptable strategy to identify the patient's position is the classification of images created from a grid of pressure sensors located in the bed. These samples can be arranged based on supervised learning methods. Usually, image conditioning is required before images are loaded into a learning method to increase classification accuracy. However, continuous monitoring of a person requires large amounts of time and computational resources if complex pre-processing algorithms are used. So, the problem is to classify the image posture of patients with different weights, heights, and positions by using minimal sample conditioning for a specific supervised learning method. In this work, it is proposed to identify the patient posture from pressure sensor images by using well-known and simple conditioning techniques and selecting the optimal texture descriptors for the Support Vector Machine (SVM) method. This is in order to obtain the best classification and to avoid image over-processing in the conditioning stage for the SVM. The experimental stages are performed with the color models Red, Green, and Blue (RGB) and Hue, Saturation, and Value (HSV). The results show an increase in accuracy from 86.9% to 92.9% and in kappa value from 0.825 to 0.904 using image conditioning with histogram equalization and a median filter, respectively. [ABSTRACT FROM AUTHOR]

Coal–gangue sorting is a vital component of intelligent mine construction. As intelligent manufacturing continued to advance, data-driven coal–gangue recognition emerged as a prominent research topic. However, conventional data-driven methods for coal–gangue recognition heavily rely on expert-extracted features. The process of feature extraction is labor-intensive and significantly impacts the final outcome. Deep learning (DL) offers an effective approach to automatically extract features from raw data. Among the various DL techniques, convolutional neural networks (CNNs) have proven to be particularly effective. In this paper, we propose an intelligent method for recognizing coal–rock by fusing multiple preprocessing techniques applied to near-infrared spectra and employing dual attention. Initially, a signal-to-RGB image conversion method is applied to fuse three types of preprocessing data, namely first-order differential, second-order differential, and standard normal transform, into an RGB image representation. Subsequently, we propose a neural network model (CBAM-VIT) that integrates the convolutional block attention mechanism (CBAM) and Vision Transformer (VIT). When evaluated on the coal–rock dataset, this model achieves an accuracy of 98.5%, surpassing the performance of VIT (95.3%), VGG-16 (89%), and AlexNet (82%). The comparative results clearly demonstrate that the proposed coal–gangue recognition method yields significant improvements in classification outcomes. [ABSTRACT FROM AUTHOR]

The present study proposes a novel deep-learning model for remote sensing image enhancement. It maintains image details while enhancing brightness in the feature extraction module. An improved hierarchical model named Global Spatial Attention Network (GSA-Net), based on U-Net for image enhancement, is proposed to improve the model's performance. To circumvent the issue of insufficient sample data, gamma correction is applied to create low-light images, which are then used as training examples. A loss function is constructed using the Structural Similarity (SSIM) and Peak Signal-to-Noise Ratio (PSNR) indices. The GSA-Net network and loss function are utilized to restore images obtained via low-light remote sensing. This proposed method was tested on the Northwestern Polytechnical University Very-High-Resolution 10 (NWPU VHR-10) dataset, and its overall superiority was demonstrated in comparison with other state-of-the-art algorithms using various objective assessment indicators, such as PSNR, SSIM, and Learned Perceptual Image Patch Similarity (LPIPS). Furthermore, in high-level visual tasks such as object detection, this novel method provides better remote sensing images with distinct details and higher contrast than the competing methods. [ABSTRACT FROM AUTHOR]

Sonar imaging technology is widely used in the field of marine and underwater monitoring because sound waves can be transmitted in elastic media, such as the atmosphere and seawater, without much interference. In underwater object detection, due to the unique characteristics of the monitored sonar image, and since the target in an image is often accompanied by its own shadow, we can use the relative relationship between the shadow and the target for detection. To make use of shadow-information-aided detection and realize accurate real-time detection in sonar images, we put forward a network based on a lightweight module. By using the attention mechanism with a global receptive field, the network can make the target pay attention to the shadow information in the global environment, and because of its exquisite design, the computational time of the network is greatly reduced. Specifically, we design a ShuffleBlock model adapted to Hourglass to make the backbone network lighter. The concept of CNN dimension reduction is applied to MHSA to make it more efficient while paying attention to global features. Finally, CenterNet's unreasonable distribution method of positive and negative samples is improved. Simulation experiments were carried out using the proposed sonar object detection dataset. The experimental results further verify that our improved model has obvious advantages over many existing conventional deep learning models. Moreover, the real-time monitoring performance of our proposed model is more conducive to the implementation in the field of ocean monitoring. [ABSTRACT FROM AUTHOR]

Biometric recognition techniques have become more developed recently, especially in security and attendance systems. Biometrics are features attached to the human body that are considered safer and more reliable since they are difficult to imitate or lose. One of the popular biometrics considered in research is palm veins. They are an intrinsic biometric located under the human skin, so they have several advantages when developing verification systems. However, palm vein images obtained based on infrared spectra have several disadvantages, such as nonuniform illumination and low contrast. This study, based on a convolutional neural network (CNN), was conducted on five public datasets from CASIA, Vera, Tongji, PolyU, and PUT, with three parameters: accuracy, AUC, and EER. Our proposed VeinCNN recognition method, called verification scheme with VeinCNN, uses hybrid feature extraction from a discrete wavelet transform (DWT) and histogram of oriented gradient (HOG). It shows promising results in terms of accuracy, AUC, and EER values, especially in the total parameter values. The best result was obtained for the CASIA dataset with 99.85% accuracy, 99.80% AUC, and 0.0083 EER. [ABSTRACT FROM AUTHOR]

Image fusion is the generation of an informative image that contains complementary information from the original sensor images, such as texture details and attentional targets. Existing methods have designed a variety of feature extraction algorithms and fusion strategies to achieve image fusion. However, these methods ignore the extraction of common features in the original multi-source images. The point of view proposed in this paper is that image fusion is to retain, as much as possible, the useful shared features and complementary differential features of the original multi-source images. Shared and differential learning methods for infrared and visible light image fusion are proposed. An encoder with shared weights is used to extract shared common features contained in infrared and visible light images, and the other two encoder blocks are used to extract differential features of infrared images and visible light images, respectively. Effective learning of shared and differential features is achieved through weight sharing and loss functions. Then, the fusion of shared features and differential features is achieved via a weighted fusion strategy based on an entropy-weighted attention mechanism. The experimental results demonstrate the effectiveness of the proposed model with its algorithm. Compared with the-state-of-the-art methods, the significant advantage of the proposed method is that it retains the structural information of the original image and has better fusion accuracy and visual perception effect. [ABSTRACT FROM AUTHOR]

A time–frequency residual convolution neural network (TFRCNN) was proposed to identify various rolling bearing fault types more efficiently. Three novel points about TFRCNN are presented as follows: First, by constructing a double-branch convolution network in the time domain and the frequency domain, the respective features in the time domain and the frequency domain were extracted to ensure the rich and complete feature representation of raw data sources. Second, specific residual structures were designed to prevent learning degradation of the deep network, and global average pooling was adopted to improve the network's sparsity. Third, TFRCNN was better than the other models in terms of prediction accuracy, robustness, generalization ability, and convergence. The experimental results demonstrate that the prediction accuracy rate of TFRCNN, trained using mixing load data, reached 98.88 to 99.92% after optimizing the initial learning rate and choosing the optimizer and loss function. It was verified that TFRCNN can adaptively learn to extract deep fault features, accurately identify bearing fault conditions, and overcome the limitations of classical shallow feature extraction and classification methods, as well as common convolution neural networks. Hence, this investigation revealed TFRCNN's potential for bearing fault diagnosis in practical engineering applications. [ABSTRACT FROM AUTHOR]