Search

Precise and content-aware style transfer in oil painting remains a significant research focus. This involves preserving the semantic content of the original image while accurately emulating the desired painting style. Developing robust algorithms for accurate recognition and classification of various patterns within oil paintings remains a challenge which identify and categorize styles, textures and visual elements to contribute comprehensive understanding of the artwork. The conventional image style transfer algorithm mainly relies on mathematical modeling to characterize image texture facilitating the combination of a content image with a style image to realize style transfer effects. These algorithms neglect the distribution of image edges leading to blurred contours in the resulting images. To address these limitations, this paper proposed a novel decoration pattern style migration algorithm based on SRCNN (Super Resolution Convolutional Neural Network) model using UNet-based architecture. The algorithm leverages the Laplace operator for image sharpening by accentuating edge distributions. Downsampling techniques are used to generate low-resolution images mitigating the protracted duration of image style transfer iterations. The incorporation of Gaussian sampling and a parallelization algorithm contributes to the efficacy of this method in facilitating the transfer of oil painting styles. The proposed model achieves better results using Facades data set, i.e., variance (6.35%), standard deviation (8.67%), uncertainty (11.21%), SSIM score (94.45%), PSNR score (40.24%), MSE score (6.21%) and accuracy (95.00%). For the Monet2photo data set, i.e., variance (7.25%), standard deviation (9.78%), uncertainty (12.78%), SSIM score (91.69%), PSNR score (43.71%), MSE score (8.67%) and accuracy (92.00%). Our proposed method outperforms classical methods using both data sets. [ABSTRACT FROM AUTHOR]

Manual identification of tomato leaf diseases is a time-consuming and laborious process that may lead to inaccurate results without professional assistance. Therefore, an automated, early, and precise leaf disease recognition system is essential for farmers to ensure the quality and quantity of tomato production by providing timely interventions to mitigate disease spread. In this study, we have proposed seven robust Bayesian optimized deep hybrid learning models leveraging the synergy between deep learning and machine learning for the automated classification of ten types of tomato leaves (nine diseased and one healthy). We customized the popular Convolutional Neural Network (CNN) algorithm for automatic feature extraction due to its ability to capture spatial hierarchies of features directly from raw data and classical machine learning techniques [Random Forest (RF), XGBoost, GaussianNB (GNB), Support Vector Machines (SVM), Multinomial Logistic Regression (MLR), K-Nearest Neighbor (KNN)], and stacking for classifications. Additionally, the study incorported a Boruta feature filtering layer to capture the statistically significant features. The standard, research-oriented PlantVillage dataset was used for the performance testing, which facilitates benchmarking against prior research and enables meaningful comparisons of classification performance across different approaches. We utilized a variety of statistical classification metrics to demonstrate the robustness of our models. Using the CNN-Stacking model, this study achieved the highest classification performance among the seven hybrid models. On an unseen dataset, this model achieved average precision, recall, f1-score, mcc, and accuracy values of 98.527%, 98.533%, 98.527%, 98.525%, and 98.268%, respectively. Our study requires only 0.174 s of testing time to correctly identify noisy, blurry, and transformed images. This indicates our approach's time efficiency and generalizability in images captured under challenging lighting conditions and with complex backgrounds. Based on the comparative analysis, our approach is superior and computationally inexpensive compared to the existing studies. This work will aid in developing a smartphone app to offer farmers a real-time disease diagnosis tool and management strategies. [ABSTRACT FROM AUTHOR]

With the development of image feature matching technology, feature matching algorithms based on deep learning have achieved excellent results, but in scenarios with low texture or extreme perspective changes, the matching accuracy is still difficult to guarantee. In this paper, a superresolution reconstruction method based on a Residual-ESPCN (efficient subpixel convolutional neural network) approach is proposed based on LoFTR (local feature matching with transformers). The superresolution method is used to improve the interpolation method used in ASFF (adaptive spatial feature fusion) to increase the image resolution, enhance the detailed information of the image, and make the extracted features richer. Then, ASFF is introduced into the local feature extraction module of LoFTR, which can alleviate the inconsistency problem of information transmission between different scale features of the feature pyramid and lessen the amount of information lost during transmission from low- to high-resolution levels. Moreover, to improve the adaptability of the algorithm to different scenarios, OTSU is introduced to adaptively calculate the threshold of feature matching. The experimental results show that in different indoor or outdoor scenarios, our proposed algorithm for matching features can effectively improve the adaptability of feature matching and can achieve good results in terms of the area under the curve (AUC), accuracy and recall. [ABSTRACT FROM AUTHOR]

The windspeed on the sea surface is an important factor affecting the process of detecting sea surface targets, as it affects the reflection and radiation intensity, posing challenges for precise detection of optical remote sensing targets. This study collects and obtains ultra-high-resolution hyperspectral data using a push-broom hyperspectral sensor, and employs a novel data processing approach to process the raw data and extract significant features related to sea surface windspeed. Based on this foundation, further exploration is conducted into the practical requirements of the spatial dimension and spectral resolution of remote sensing sensors. Dimensionality reduction processing is applied to the hyperspectral data in both spatial and spectral dimensions. It is noteworthy that certain low-resolution spectral extractions will be derived from the central wavelengths of AVIRIS and Landsat satellites, and generate spatial-spectral joint heatmaps (SSHMs) capable of depicting sea surface windspeeds, so as to assess the potential of windspeed estimation utilizing these sensors. Moreover, four kinds of convolutional neural network (CNN) architectures, in conjunction with transfer learning techniques, are utilized to achieve precise estimation of sea surface windspeed. The final estimation results show that CNN models achieve reliable performance until the spectral dimension is reduced to 8 or below, and the spatial dimension is reduced to 25; The lightweight framework effectively employs coarse-resolution SSHMs to extract windspeed and achieve accurate recognition results. This paper introduces a feasibility study for windspeed estimation utilizing the optical sensors that meet the spatial and spectral resolution requirements. [ABSTRACT FROM AUTHOR]

The integration of convolutional neural network (CNN) and transformer enhances the network's capacity for concurrent modeling of texture details and global structures. However, training challenges with transformer limit their effectiveness to low-resolution images, leading to increased artifacts in slightly larger images. In this paper, we propose a single-stage network utilizing large kernel attention (LKA) to address high-resolution damaged images. LKA enables the capture of both global and local details, akin to transformer and CNN networks, resulting in high-quality inpainting. Our method excels in: (1) reducing parameters, improving inference speed, and enabling direct training on 1024 × 1024 resolution images; (2) utilizing LKA for enhanced extraction of global high-frequency and local details; (3) demonstrating excellent generalization on irregular mask models and common datasets such as Places2, Celeba-HQ, FFHQ, and the random irregular mask dataset Pconv from NVIDIA. [ABSTRACT FROM AUTHOR]

Image segmentation is a critical and challenging endeavor in the field of medicine. A magnetic resonance imaging (MRI) scan is a helpful method for locating any abnormal brain tissue these days. It is a difficult undertaking for radiologists to diagnose and classify the tumor from several pictures. This work develops an intelligent method for accurately identifying brain tumors. This research investigates the identification of brain tumor types from MRI data using convolutional neural networks and optimization strategies. Two novel approaches are presented: the first is a novel segmentation technique based on firefly optimization (FFO) that assesses segmentation quality based on many parameters, and the other is a combination of two types of convolutional neural networks to categorize tumor traits and identify the kind of tumor. These upgrades are intended to raise the general efficacy of the MRI scan technique and increase identification accuracy. Using MRI scans from BBRATS2018, the testing is carried out, and the suggested approach has shown improved performance with an average accuracy of 98.6%. [ABSTRACT FROM AUTHOR]

Recently, convolutional neural networks (CNNs) have facilitated the rapid development of image super-resolution. Most deep networks are challenging to apply to the real world due to their high cost of memory storage and computational complexity. This paper addresses this issue by proposing a lightweight subpixel sampling network (SSN). Specifically, we use a traditional encoder-decoder structure and replace the deconvolution and pooling layers by subpixel up-sampling and down-sampling without parameters. Subpixel sampling retains more image information than other sampling methods. In addition, we propose parsimonious spatial and channel attention blocks through which multi-scale features are fused and more image textures can be recovered. Through extensive experiments, we validate the effectiveness of subpixel sampling, spatial attention block, and channel attention block. In terms of quantitative metrics and visual quality, our models achieve performance comparable to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Texture filtering attempts to retain salient structures and remove insignificant textures. In this paper, we propose a highly effective iterative unsupervised deep bilateral texture filtering neural network for texture smoothing. The bilateral texture loss function is introduced to train the model without the ground truth smoothing images for guidance. The proposed model inherits well-known advantages of the bilateral texture filter to capture the texture information effectively. The model is trained solely using the training data, then the predicted outputs are generated iteratively through multiple forward passes. Extensive experiments demonstrate that our proposed iterative unsupervised deep bilateral texture filtering neural network outperforms existing methods in effectively removing textures while preserving the main structures of the image. The results showcase the superior performance of our approach and its ability to achieve high-quality texture smoothing without sacrificing important image features. [ABSTRACT FROM AUTHOR]

Deep learning (DL)-based categorization and detection methods for breast cancer diagnosis through medical images are utilized by computer-aided diagnosis (CAD) techniques. In this study, two DL-based ensemble models were proposed to improve the diagnostic efficiency of the system. The proposed models were evaluated on two mammography datasets: DDSM and CBIS-DDSM. To enhance the performance of breast lesion classification from mammographic scans, three pretrained convolutional neural network (CNN) models, namely VGG16, InceptionV3, and VGG19, were used as base classifiers, and two ensemble models were trained. Ensemble Model 1, using a linear meta-learner in the form of logistic regression for classification, and Ensemble Model 2, using a neural net as the meta-learner for classification. The DDSM dataset achieved accuracy, sensitivity, and specificity of 98.02%, 97.17%, and 98.87%, respectively, for Ensemble Model 1, and 98.10%, 97.01%, and 99.12%, respectively, for Ensemble Model 2. Furthermore, the performance of the proposed models was compared with existing state-of-the-art systems, and the results showed an increase in breast cancer classification performance with the proposed models. Hence, the proposed models have the potential to aid medical professionals in accurately classifying breast lesions. [ABSTRACT FROM AUTHOR]

Cracks are the primary indicator informing the structural health of concrete structures. Frequent inspection is essential for maintenance, and automatic crack inspection offers a significant advantage, given its efficiency and accuracy. Previously, image-based crack detection systems have been utilized for individual images, yet these systems are not effective for large inspection areas. This paper thereby proposes an image-based crack detection system using a Deep Convolution Neural Network (DCNN) to identify cracks in mosaic images composed from UAV photos of concrete footings. UAV images are transformed into 3D footing models, from which the composite images are created. The CNN model is trained on 224 × 224 pixel patches, and training samples are augmented by various image transformation techniques. The proposed method is applied to localize cracks on composite images through the sliding window technique. The proposed VGG16 CNN detection system, with 95% detection accuracy, indicates superior performance to feature-based detection systems. [ABSTRACT FROM AUTHOR]

In recent years, automatic image classification methods have significantly progressed, notably black box algorithms such as machine learning and deep learning. Unfortunately, such efforts only focused on improving performance, rather than attempting to explain and interpret how classification models actually operate. This article compares three state-of-the-art algorithms incorporating random forests, gradient boosting and convolutional neural networks for geomorphological mapping. It also attempts to explain how the most effective classifier makes decisions by evaluating which of the geomorphometric variables are most important for automatic mapping and how they affect the classification results using one of the explainable artificial intelligence techniques, namely accumulated local effects (ALE). This method allows us to understand the relationship between predictors and the model's outcome. For these purposes, eight sheets of the digital geomorphological map of Poland on the scale of 1:100,000 were used as the reference material. The classification results were validated using the holdout method and cross-validation for individual sheets representing different morphogenetic zones. The terrain elevation entropy, absolute elevation, aggregated median elevation and standard deviation of elevation had the greatest impact on the classification results among the 15 geomorphometric variables considered. The ALE analysis was conducted for the XGBoost classifier, which achieved the highest accuracy of 92.8%, ahead of Random Forests at 84% and LightGBM at 73.7% and U-Net at 59.8%. We conclude that automatic classification can support geomorphological mapping only if the geomorphological characteristics in the predicted area are similar to those in the training dataset. The ALE plots allow us to analyze the relationship between geomorphometric variables and landform membership, which helps clarify their role in the classification process. [ABSTRACT FROM AUTHOR]

Display technologies have evolved over the years. It is critical to develop practical HDR capturing, processing, and display solutions to bring 3D technologies to the next level. Depth estimation of multi-exposure stereo image sequences is an essential task in the development of cost-effective 3D HDR video content. In this paper, we develop a deep architecture for multi-exposure stereo depth estimation. The proposed architecture has two novel components. First, the stereo matching technique used in traditional stereo depth estimation is revamped. For the stereo depth estimation component of our architecture, a mono-to-stereo transfer learning approach is deployed. The proposed formulation circumvents the cost volume construction requirement, which is replaced by a dual-encoder single-decoder CNN with different weights for feature fusion. EfficientNet-based blocks are used to learn the disparity. Secondly, we combine disparity maps obtained from the stereo images at different exposure levels using a robust disparity feature fusion approach. The disparity maps obtained at different exposures are merged using weight maps calculated for different quality measures. The final predicted disparity map obtained is more robust and retains best features that preserve the depth discontinuities. The proposed CNN offers flexibility to train using standard dynamic range stereo data or with multi-exposure low dynamic range stereo sequences. In terms of performance, the proposed model surpasses state-of-the-art monocular and stereo depth estimation methods, both quantitatively and qualitatively, on challenging Scene flow and differently exposed Middlebury stereo datasets. The architecture performs exceedingly well on complex natural scenes, demonstrating its usefulness for diverse 3D HDR applications. [ABSTRACT FROM AUTHOR]

Any kidney dimension and volume variation can be a remarkable indicator of kidney disorders. Precise kidney segmentation in standard planes plays an undeniable role in predicting kidney size and volume. On the other hand, ultrasound is the modality of choice in diagnostic procedures. This paper proposes a convolutional neural network with nested layers, namely Fast-Unet++, promoting the Fast and accurate Unet model. First, the model was trained and evaluated for segmenting sagittal and axial images of the kidney. Then, the predicted masks were used to estimate the kidney image biomarkers, including its volume and dimensions (length, width, thickness, and parenchymal thickness). Finally, the proposed model was tested on a publicly available dataset with various shapes and compared with the related networks. Moreover, the network was evaluated using a set of patients who had undergone ultrasound and computed tomography. The dice metric, Jaccard coefficient, and mean absolute distance were used to evaluate the segmentation step. 0.97, 0.94, and 3.23 mm for the sagittal frame, and 0.95, 0.9, and 3.87 mm for the axial frame were achieved. The kidney dimensions and volume were evaluated using accuracy, the area under the curve, sensitivity, specificity, precision, and F1. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks can effectively improve the performance of single-image super-resolution reconstruction. Deep networks tend to achieve better performance than others. However, the deep CNNs will lead to a dramatic increase in the size of parameters, limiting its application on embedding and resource-constrained devices, such as smart phone. To address the common problems of blurred image edges, inflexible convolution kernel size selection and slow convergence during training procedure due to redundant network structure in image super-resolution algorithms, this paper proposes a lightweight single-image super-resolution network that fuses multi-level features. The components are mainly two-level nested residual blocks. To better extract features and reduce the number of parameters, each residual block adopts an asymmetric structure. Firstly, it expands twice and then compresses the number of channels twice. Secondly, in the residual block, the feature information of different channels is weighted and fused by adding an autocorrelation weight unit. The quality of the reconstructed image of the proposed method is superior to the existing image super-resolution reconstruction methods in both subjective perception and objective evaluation indicators, and the reconstruction performance is better when the factor is large. [ABSTRACT FROM AUTHOR]

Designing reasonable architectures of convolutional neural network (CNN) for specific image segmentation remains a challenging task, as the determination of the structure and hyperparameters of CNN depends heavily on expertise and requires a great deal of time. Evolutionary algorithm (EA) has been successfully applied to the automatic design of CNNs; however, the inherent stochastic search of EA tends to cause "experience loss" and requires very large computational resources. To deal with this problem, a maximal sparse convex surrogate model with updated empirical information is proposed in this paper to guide the evolutionary process of CNN design. This sparse convex function is transformed from a non-convex function to a maximized sparse convex function, which can better utilize the prior empirical knowledge to assist the evolutionary search. In addition, a balance strategy between computational resources and accuracy is proposed in the selection of reasonable network architectures. The proposed fully automatic design method of CNN is applied to the segmentation of steel microstructure images, and experimental results demonstrate that the proposed method is competitive with the existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Rapid advancement in the sophistication of malware has posed a serious impact on the device connected over the Internet. Malware writing is driven by economic benefits; thus, an alarming increase in malware variants is witnessed. Recently, a large volume of malware attacks are reported on Internet of Things (IoT) networks; as these devices are exposed to insecure segments, further IoT devices reported have hardcoded credentials. To combat malware attacks on mobile devices and desktops, deep learning-based detection approaches have been attempted to detect malware variants. The existing solutions require large computational overhead and also have limited accuracy. In this paper, we visualize malware as Markov images to preserve semantic information of consecutive pixels. We further extract textures from Markov images using Gabor filter (named as Gabor images), and subsequently develop models using VGG-3 and Densely Connected Network (DenseNet). To encourage real-time malware detection and classification, we fine-tune Densely Connected Network. These models are trained and evaluated on two datasets namely Malimg and BIG2015. In our experimental evaluations, we found that DenseNet identifies Malimg and BIG2015 samples with accuracies of 99.94% and 98.98%, respectively. Additionally, the performance of our proposed method in classifying malware files to their respective families is superior compared to the state-of-the approach calibrated using prediction time, F1-score, and accuracy. [ABSTRACT FROM AUTHOR]

Purpose: 2-[18F]FDG PET/CT plays an important role in the management of pulmonary nodules. Convolutional neural networks (CNNs) automatically learn features from images and have the potential to improve the discrimination between malignant and benign pulmonary nodules. The purpose of this study was to develop and validate a CNN model for classification of pulmonary nodules from 2-[18F]FDG PET images. Methods: One hundred thirteen participants were retrospectively selected. One nodule per participant. The 2-[18F]FDG PET images were preprocessed and annotated with the reference standard. The deep learning experiment entailed random data splitting in five sets. A test set was held out for evaluation of the final model. Four-fold cross-validation was performed from the remaining sets for training and evaluating a set of candidate models and for selecting the final model. Models of three types of 3D CNNs architectures were trained from random weight initialization (Stacked 3D CNN, VGG-like and Inception-v2-like models) both in original and augmented datasets. Transfer learning, from ImageNet with ResNet-50, was also used. Results: The final model (Stacked 3D CNN model) obtained an area under the ROC curve of 0.8385 (95% CI: 0.6455–1.0000) in the test set. The model had a sensibility of 80.00%, a specificity of 69.23% and an accuracy of 73.91%, in the test set, for an optimised decision threshold that assigns a higher cost to false negatives. Conclusion: A 3D CNN model was effective at distinguishing benign from malignant pulmonary nodules in 2-[18F]FDG PET images. [ABSTRACT FROM AUTHOR]

In ophthalmology, early fundus screening is an economical and effective way to prevent blindness from eye diseases. Because clinical evidence does not exist, manual detection is time-consuming and may cause the situation to be delayed clinically. With the development of deep learning, a wide variety of eye diseases have shown promising results; however, most of these studies focus on only one disease. Therefore, focusing on multi-disease classification based on fundus images is an effective approach. Consequently, this paper presents a method based on the multilevel glowworm swarm optimization convolutional neural network (MGSCNN) for the classification of multiple diseases. It is proposed that the proposed system has two stages, namely preprocessing and classification. In the beginning, the images are normalized, smoothed, and resized to prepare them for preprocessing. After pre-processing, the images are fed to the MGSCNN classifier to classify an image as normal or abnormal (covering 39 different types of diseases). In the CNN classifier, with the help of Glowworm Swarm Optimizer (GSO), we optimally detect the structure and hyperparameters of CNN simultaneously. This approach achieves an excellent accuracy of 95.09% based on various metrics. [ABSTRACT FROM AUTHOR]

The mango crop production and quality are affected by several factors. Plant disease is one of the prime factors that impact crop yield. The disease impacts all parts of plants such as leaves, roots, and stems. Farmers waste most of their energy, time, and effort dealing with it manually and lead to heavy loss in the yield of the crops. Mango production is affected by several types of diseases. Prior recognition of disease would ease and reduce the diagnostic process that enhances the production of quality crops. A huge investigation has been carried out in the field of identifying and classifying leaf disease. Hence, an appropriate artificial intelligence (AI) based solution is needed to support farmers. The objective of the work is to provide an effective and efficient AI-based solution to detect and classify leaf disease earlier. Earlier disease detection in crops is the most prominent way to prevent loss of money and time. The plant leaf image is mainly used as a source to detect disease in the plant. This paper employs an ensemble stacked deep learning model to resolve the problem of automatic identification of mango-leaf diseases. In the proposed approach, initially, the images are segmented for the region of interest and input to a stack of various deep neural networks. The outcome of the deep neural network is aggregated with a machine learning model to identify leaf disease. This model is used to identify various mango leaf diseases such as Powdery mildew, Anthracnose, etc. In the experiment, the deep learning models are stacked and aggregated with machine learning to identify mango leaf diseases. The proposed model is compared with state-of-the-art models and outperforms with 98.57% accuracy. [ABSTRACT FROM AUTHOR]