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The Mars Reconnaissance Orbiter and its Context Camera (CTX) have acquired more than 100,000 separate panchromatic images that capture nearly the entire surface of Mars at ∼5–6 m/pixel. This paper describes a data processing workflow used to generate the first contiguous global mosaic of CTX data, which represents a large improvement in spatial resolution over existing 100 m/pixel contiguous global mosaics. We describe the overarching strategy for the mosaic's construction, which was to maximize the scientific utility of a continuous mosaic that is 5.7 trillion pixels in size. The pipeline used for data processing prioritized traceability and reproducibility of the final mosaic, such that the provenance of all pixels is reported, equipping scientists with information to differentiate mosaic artifacts from surface landforms and to incorporate critical image metadata into their analyses. The CTX data set synthesized into a global CTX mosaic facilitates ready analysis and provides a new capability in transitioning global studies of Mars from high‐resolution investigations of individual images to systematic studies of the entire Martian surface at outcrop‐resolving quality without regard to image boundaries. Plain Language Summary: We generated a global mosaic of Mars using Context Camera satellite imagery at 5.0 m/pixel, a substantial improvement in spatial resolution over the previous global mosaic at 100 m/pixel. This paper describes a new technique for merging overlapping images together in a way that preserves all information from each component image and produces a map of all image boundaries that is included with the mosaic. This makes scientific analyses that are facilitated by the mosaic fully traceable and reproducible. With this mosaic, scientists can now readily analyze landforms without the overhead of downloading individual images and perform complete global analyses of Mars at a resolution capable of seeing rock outcrops on the surface. Key Points: A global mosaic of Mars at 5.0 m/pixel has been created from 86,571 Context Camera images that are co‐registered to each other and seam‐correctedNon‐destructive image processing was used to preserve all image metadata and map seamsThe new mosaic will facilitate systematic studies of Mars at 5.0 m/pixel spatial resolution [ABSTRACT FROM AUTHOR]

The discrepancy in data distribution between training and testing scenarios, as well as the inductive bias of convolutional neural networks towards image styles, reduces the model's generalization ability. Many unsupervised domain generalization methods based on feature decoupling suffer from an initial neglect of explicit decoupling of content and style features, resulting in content features that still contain considerable redundant information, thereby restricting improvements in generalization capability. To tackle this problem, this paper optimizes the learning process of domain‐invariant (content) features into an information compression issue, minimizing redundancy in content features. Furthermore, to enhance decoupled learning, this paper introduces innovative cross‐domain loss functions and image reconstruction modules that explicitly decouple and merge content and style across different domains. Extensive experiments demonstrate the method's significant enhancements over recent cutting‐edge approaches. [ABSTRACT FROM AUTHOR]

Current research on apron conflict detection is often limited to the interaction between the aircraft as a whole and other objects, making it difficult to accomplish targeted identification of vulnerable and high‐cost aircraft components. However, the implementation of detailed aircraft identification is of great significance to enhance the safety of airport surface operations. Based on the excellent performance of High‐Resolution Network (HRNet) in keypoint detection, a lightweight end‐to‐end keypoint detection network, namely Squeeze and Excitation High‐Resolution Network (SEHRNet), is proposed in this paper to solve the problems of HRNet's slower computation and more redundancy. First, the errors arising from coordinate transformations in the coding and decoding process are solved by an improved feature map coding and decoding process. Second, replace the BasicBlock in HRNet with the Depthwise separable convolutions based on the Squeeze‐and‐Excitation Networks, which drastically cuts the computational cost of the network. Third, the improved Bottleneck module is used to further enhance the capability of feature extraction. Experimental results prove that, based on the aircraft keypoint detection dataset, the SEHRNet proposed in this paper shows stronger applicability compared to the current mainstream networks. Compared with the original HRNet, the improved network has higher accuracy, faster speed, and lighter model for aircraft keypoint detection. [ABSTRACT FROM AUTHOR]

Currently, deep learning methods for low‐light image enhancement tasks mainly focus on the illumination of images, while neglecting the problems of image noise and feature loss. To address this issue, this paper proposes a novel low‐light image enhancement network called DAF‐Retinex, based on the Retinex‐Net. To address the issue of image noise, different from traditional image denoise methods, this paper utilizes a fully convolutional neural network to denoise the reflection component, additionally, a denoising loss function is introduced to suppress noise. For preserving image details and extracting features, this paper creatively introduces self‐calibrated convolutions into low‐light image enhancement tasks, furthermore, a feature augmented attention block consisting of feature‐guided attention (FGA) is designed for feature learning to effectively enhance image illumination and extract image detail features. Experimental results demonstrate that the proposed algorithm in this paper effectively removes image noise and extracts detailed features, resulting in visually improved outcomes. On public datasets, the average improvement in objective evaluation metrics of image quality such as PSNR, SSIM, and NIQE are 1.13%, 4.12%, and 1.28%, respectively. [ABSTRACT FROM AUTHOR]

Segmentation is crucial in diagnosing retinal diseases by accurately identifiying retinal vessels. This paper addresses the complexity of segmenting retinal vessels, highlighting the need for precise analysis of blood vessel structures. Despite the progress made by convolutional neural networsks (CNNs) in image segmentation, their limitations in capturing the global structure of retinal vsessels and maintaining segmentation continuity present challenges. To tackle these issues, our proposed network integrates graph convolutional networks (GCNs) and attention mechansims. This allows the model to consider pixel relationships and learn vessel graphical structures, significantly improving segmentation accuracy. Additionally, the attentional feature fusion module, including pixel‐wise and channel‐wise attention mechansims within the U‐Net architecture, refines the model's focus on relevant features. This paper emphasizes the importance of continuty preservation, ensuring an accurate representation of pixel‐level information and structural details during sefmentation. Therefore, our method performs as an effective solution to overcome challenges in retinal vessel segmentation. The proposed method outperformed the state‐of‐the‐art approaches on DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (Structed Analysis of the Retina) datasets with accuracies of 0.12% and 0.14%, respecttively. Importantly, our proposed approach excelled in delineating slender and diminutive blood vessels, crucial for diagnosing vascular‐related diseases. Implementation is accessible on https://github.com/CVLab‐SHUT/VGA‐Net. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Computer Vision, discussing the topic of advanced image restoration and enhancement. The editorial highlights the challenges faced in this field, such as the complexity of degradation models for real-world low-quality images and the difficulty of acquiring paired data. It also introduces a special issue of the journal that includes five accepted papers, which focus on video reconstruction and image super-resolution. The editorial concludes by providing brief summaries of each accepted paper. The guest editors of the special issue are also mentioned, along with their research interests and affiliations. [Extracted from the article]

Synthetic aperture radar (SAR) image change detection suffers from poor quality of the difference image and low detection accuracy. Hence, this paper proposes a SAR image change detection method based on a fused difference image and an optimized random forest scheme, termed LRN‐SSARF. Specifically, a fusion operator difference image LRN is proposed, which is generated using a weighted fusion of log‐ratio (LR), ratio (R), and normalized ratio (NoR). This difference image generation method reduces noise's influence. Then, the Otsu algorithm is applied to segment the difference image and select the training samples. The training samples are input into the random forest (RF) model optimised by the sparrow search algorithm (SSA) for training and classification. Finally, the region link is uesd to refine the detection results and generate the final result. The change detection results of six real SAR image scenes highlight that the proposed algorithm has a high detection accuracy, and affords appealing integrity and detailed information about the change regions. Specially, the detection accuracy advantage of the Bangladesh dataset is larger, with the accuracy and Kappa coefficient reaching 98.04% and 92.00%, much higher than the competitor methods. [ABSTRACT FROM AUTHOR]

Medical image colouring techniques enable to colourize grey‐scale medical images for assisting doctors in diagnosis. Benefiting from the non‐linear fitting ability of deep neural network, deep medical image colouring techniques have achieved remarkable results. However, existing methods are still facing content and structure feature leakage, unrealistic colouring and poor scale invariability. Thus, this paper, proposes a Transformer‐based medical image colouring algorithm with long‐term dependency to avoid feature leakage of coloured images. To be specific, this method employs two different Transformer encoders to generate and encode feature sequences for grey‐scale medical images and real human colour slice images, respectively. Then, a novel multi‐layer Transformer decoder is used to stylize grey‐scale map image features based on the real physical colour feature sequences. For colouring images at different scales, we implement content‐ aware positional encoding with scale invariance and propose style‐aware positional encoding strategy to take realistic and physical colour prior into account. Extensive experimental results indicate our method has achieved better colourization effects than recent state‐of‐the‐art medical image colourization methods. [ABSTRACT FROM AUTHOR]

Fires not only cause devastating consequences for human life and property, but also lead to soil erosion in forests. Therefore, it is necessary to design a novel algorithm that can quickly monitor smoke from fires. Most existing smoke segmentation methods do not consider the segmentation accuracy of algorithms under limited computational resources. To address this research gap, this paper proposes a lightweight smoke segmentation algorithm based on DeepLabV3+ that achieves fast inference speed and high accuracy for different sizes smoke. To reduce the number of parameters, the feature extraction network of the DeeplabV3+ algorithm is replaced by MobileNetV2, which enhances the extraction ability of the algorithm in segment smoke images. Then, the Convolutional Block Attention Module (CBAM) is added to the encoder part to enhance the perception of the algorithm for small smoke and effectively alleviates smoke mis‐segmentation. Furthermore, a newly designed loss function is used in the network. The experimental results show that the proposed method has improved by 1.27% in Smoke IoU and 1.21% in mPA compared with other methods. The weight size has been reduced to 10.76% of the original DeepLabV3+, and the inference time is only 33.71ms. Therefore, it is a more suitable early fire detection algorithm for resource‐constrained environments. [ABSTRACT FROM AUTHOR]

Due to differences in the quantity and size of observed targets, hyperspectral images are characterized by class imbalance. The standard deep learning classification model training scheme optimizes the overall classification error, which may lead to performance imbalance between classes in hyperspectral image classification frameworks. Therefore, a novel factor annealing decoupling compositional training method is proposed in this paper. Without requiring resampling or reweighting, it implicitly modulates the training process, so standard models can sufficiently learn the representation of the minority classes and further be trained as robust classifiers. Specifically, the label‐distribution‐aware margin loss is combined with the error‐rate‐based cross‐entropy loss via combination factor, which considers both imbalanced data representation learning and classifier overall performance. Then, a factor annealing optimization training scheme is designed to adjust the combination factor, which solves the stage division problem of two‐stage decoupling learning. Experimental results on two hyperspectral image datasets demonstrate that, as compared with other competing approaches, the proposed method can continuously and stably optimize the model parameters, achieving improvements in class average metrics and difficult classes without affecting overall classification performance. [ABSTRACT FROM AUTHOR]

Synthetic images facilitate stereo matching. However, synthetic images may suffer from image distortion, domain bias, and stereo mismatch, which would significantly restrict the widespread use of stereo matching models in the real world. The first goal in this paper is to synthesize real‐looking images for minimizing the domain bias between the synthesized and real images. For this purpose, sharpened disparity maps are produced from a mono real image. Then, stereo image pairs are synthesized using these imperfect disparity maps and the single real image in the proposed pipeline. Although the synthesized images are as realistic as possible, the domain styles of the synthesized images are always very different from the real images. Thus, the second goal is to enhance the domain generalization ability of the stereo matching network. For that, the feature extraction layer is replaced with a teacher–student model. Then, a constraint of binocular contrast features is imposed on the output of the model. When tested on the KITTI, ETH3D, and Middlebury datasets, the accuracy of the method outperforms traditional methods by at least 30%. Experiments demonstrate that the approaches are general and can be conveniently embedded into existing stereo networks. [ABSTRACT FROM AUTHOR]

Lesion segmentation is a fundamental task in medical image processing, often facing the challenge of subtle lesions. It is important to detect these lesions, even though they can be difficult to identify. Convolutional neural networks, an effective method in medical image processing, often ignore the relationship between lesions, leading to topological errors during training. To tackle topological errors, move is made from pixel‐level to hypergraph representations. Hypergraphs can model lesions as vertices connected by hyperedges, capturing the topology between lesions. This paper introduces a novel dynamic hypergraph learning strategy called DHLS. DHLS allows for the dynamic construction of hypergraphs contingent upon input vertex variations. A hypergraph global‐aware segmentation network, termed HGSNet, is further proposed. HGSNet can capture the key high‐order structure information, which is able to enhance global topology expression. Additionally, a composite loss function is introduced. The function emphasizes the global aspect and the boundary of segmentation regions. The experimental setup compared HGSNet with other advanced models on medical image datasets from various organs. The results demonstrate that HGSNet outperforms other models and achieves state‐of‐the‐art performance on three public datasets. [ABSTRACT FROM AUTHOR]

In recent years, low‐light image enhancement has become increasingly active. However, in underground mine environments, acquiring high‐quality images is still challenging due to low light, low contrast, and occlusion. To address this problem, this study proposes a low‐light image enhancement method for underground mine based on generative adversarial networks (UM‐GAN), which aims to take full advantage of the ability of GAN to achieve the restoration of details, the reduction of noise, and the improvement of overall image quality. The model proposed in this paper is divided into three main parts. Initially, a generator network adopting an encoder–decoder structure is developed. Subsequently, a novel strategy is introduced to merge information by utilizing inverted greyscale images and low‐light images as inputs. Further image quality enhancement is achieved by incorporating a noise reduction module employing the diffusion model. To ascertain the efficacy of the UM‐GAN method, evaluations are conducted on diverse real‐world and synthetic datasets, juxtaposing the approach against superior methods. Through qualitative and quantitative comparative experiments, the method showcases noteworthy advancements through qualitative and quantitative comparative experiments, substantiating its effectiveness. This research provides new ideas and methods for overcoming image quality problems in underground mine environments and contributes to the development of underground mine image processing. [ABSTRACT FROM AUTHOR]

Slug flow processes draw much attention in chemical and pharmaceutical manufacturing thanks to their ability to eliminate downtime costs and batch‐to‐batch variation. Using in‐line video can monitor the current process status and improve the stability of the slug flow. However, there are no effective image processing methods aiming at such videos from chemical experiments due to limited training sample size and the variety of experimental settings. The paper proposes a training‐free method to automatically detect and measure the slugs from in‐line videos. Multiple image features are fused to identify the slug shapes under various lighting conditions, a six‐point model is fitted to achieve better volume estimation, and a consistency score is estimated to quantify the detection uncertainty. The proposed algorithm achieves similar results to manual labeling for various types of slug flow processes, improving the accuracy of slug column estimation by a large margin compared to the existing automatic methods. Finally, using it to monitor the slug flow process's stability and the change of the slug volume in an injection point are demonstrated. The method not only shows that handcrafted image features have the capability for detection and segmentation from chemical experiment images but also paves the road for a training‐based algorithm for the task. [ABSTRACT FROM AUTHOR]

Perception systems in autonomous vehicles need to accurately detect and classify objects within their surrounding environments. Numerous types of sensors are deployed on these vehicles, and the combination of such multimodal data streams can significantly boost performance. The authors introduce a novel sensor fusion framework using deep convolutional neural networks. The framework employs both camera and LiDAR sensors in a multimodal, multiview configuration. The authors leverage both data types by introducing two new innovative fusion mechanisms: element‐wise multiplication and multimodal factorised bilinear pooling. The methods improve the bird's eye view moderate average precision score by +4.97% and +8.35% on the KITTI dataset when compared to traditional fusion operators like element‐wise addition and feature map concatenation. An in‐depth analysis of key design choices impacting performance, such as data augmentation, multi‐task learning, and convolutional architecture design is offered. The study aims to pave the way for the development of more robust multimodal machine vision systems. The authors conclude the paper with qualitative results, discussing both successful and problematic cases, along with potential ways to mitigate the latter. [ABSTRACT FROM AUTHOR]

This paper presents a textile defect detection method that utilizes a multi‐proportion spatial attention mechanism and channel memory feature fusion network by addressing the difficulties presented by complicated shapes and large size variations. In particular, a multi‐proportion spatial attention mechanism (MPAM) is introduced, which employs multi‐proportion convolution to improve the backbone network's capacity to detect non‐uniform structural defects. Additionally, the generality and adaptability of the model are enhanced by a multi‐scale spatial pyramid pooling structure (MS‐SPP). Second, a channel attention mechanism‐based memory feature fusion network is developed, which incorporates channel attention to adaptive weight the feature channels, focusing on crucial information channels to efficiently fuse contextual features and enhance the model's memory capacity. Finally, a novel efficient Wise‐IoU (EWIoU) loss function is proposed, which utilizes a dynamic non‐monotonic focusing mechanism to increase the penalty on distance measurement, thus enhancing the model's detection performance. Experiment findings on the ZJU‐Leaper and Tianchi textile datasets reveal that compared to the YOLOv7 baseline, the method in this paper has an increase of 6.5 and 2 percentage points, respectively, and the detection accuracy is better than most existing networks. [ABSTRACT FROM AUTHOR]

In order to balance accuracy and real‐time performance in semantic segmentation, this paper proposes a real‐time semantic segmentation algorithm model based on attention mechanism and multi‐branch feature fusion using Fast convolutional neural network model (Fast‐SCNN). In this method, the spatial detail feature enhancement branch is introduced to enhance spatial detail features firstly. Then, through rational design of fusion module, the feature information of each branch is optimized to achieve better fusion of deep and shallow features. At the end of the feature fusion module, an adaptive feature enhancement focus module is introduced to capture the interdependence between remote pixels. The experimental results show that the proposed algorithm achieves 71.55% segmentation accuracy on Cityscapes dataset, the reasoning speed FPS is 97.6 frames/s, and the number of parameters is 1.39 M, which verifies the effectiveness of the network model constructed by the algorithm. Code is available at https://github.com/ccchhheeennn/model. [ABSTRACT FROM AUTHOR]

Whole brain segmentation from magnetic resonance images (MRI) is crucial in diagnosing brain diseases and analyzing neuroimaging data. Despite advances through deep learning, challenges such as uneven gray distribution and the presence of artifacts still present hurdles in medical image processing. These limitations are often a result of insufficient spatial contextual information and lack of attention to important regions within existing models. To address these issues, this paper presents SSA‐UNet (Squeeze‐and‐Excitation and Self‐Attention UNet), a uniquely designed deep convolutional neural network that integrates spatial constraints by converting three consecutive 2D MRI slices into a single 2.5D image. This facilitates capturing inter‐slice dependencies effectively. Additionally, the newly formulated SSA block, which sequentially incorporates channel attention and Self‐Attention mechanisms, is placed before the decoders in the conventional U‐Net architecture. This enables the network to automatically weight different feature maps and focus more effectively on regions requiring precise segmentation. Rigorous evaluations on LPBA40 and IBSR18 datasets substantiate the remarkable improvements in accuracy and stability achieved by SSA‐UNet. Results indicate Dice coefficients of 98.38% and 97.47%, specificity of 99.69% and 99.57%, and sensitivity of 98.5% and 97.98% for the respective datasets. Compared to other existing models, SSA‐UNet shows significant improvements on both the LPBA40 and IBSR18 datasets. On the LPBA40 dataset, SSA‐UNet's Dice coefficient improved by 0.33% compared to the sub‐optimal model, while on the IBSR18 dataset, the improvement reached 1.78%. These empirical findings demonstrate SSA‐UNet's heightened capability in addressing the long‐standing challenges in MRI‐based whole‐brain segmentation. [ABSTRACT FROM AUTHOR]

In the field of scientific visualization, the upscaling of time‐varying volume is meaningful. It can be used in in situ visualization to help scientists overcome the limitations of I/O speed and storage capacity when analysing and visualizing large‐scale, time‐varying simulation data. This paper proposes self‐attention residual network‐based spatial super‐resolution (SARN‐SSR), a spatial super‐resolution model based on self‐attention residual networks that can generate time‐varying data with temporal coherence. SARN‐SSR consists of two components: a generator and a discriminator. The generator takes the low‐resolution volume sequences as the input and gives the corresponding high‐resolution volume sequences as the output. The discriminator takes both synthesized and real high‐resolution volume sequence as the input and gives a matrix to predict the realness as the output. To verify the validity of SARN‐SSR, four sets of time‐varying volume datasets are applied from scientific simulation. In addition, SARN‐SSR is compared on these datasets, both qualitatively and quantitatively, with two deep learning‐based techniques and one traditional technique. The experimental results show that by using this method, the closest time‐varying data to the ground truth can be obtained. [ABSTRACT FROM AUTHOR]

Pixel‐level labeling of regions of interest in an image is a key step in building a labeled training dataset for supervised deep learning networks of images. However, traditional manual labeling of cancerous regions in digital pathological images by doctors is time‐consuming and inefficient. To address this issue, this paper proposes an automatic labeling method for whole slide images, which combines clustering and edge detection techniques. The proposed method utilizes the multi‐level feature fusion model and the Long‐Short Term Memory network to discriminate the cancerous nature of the whole slide images, thereby improving the classification accuracy of the whole slide images. Subsequently, the automatic labeling of cancerous regions is achieved by integrating a density‐based clustering algorithm and an edge point extraction algorithm, both based on the discriminated results of the cancerous properties of whole slide images. The experimental results demonstrate the effectiveness of the proposed method, which offers an efficient and accurate solution to the challenging task of cancerous region labeling in digital pathological images. [ABSTRACT FROM AUTHOR]

Existing segmentation‐based methods have made considerable progress in arbitrarily shaped text detection due to the advantage of dealing with shape variation. However, there still exist challenges to detecting accurate text instances with dense layouts, inaccurate annotations, and complex backgrounds. Many recent works have focused on improving arbitrary boundary prediction, but it may be difficult to accurately distinguish each instance of dense layouts because their boundary pixels may be mistakenly classified to produce inaccurate results (i.e., adhesive texts) with inaccurate annotation and complex backgrounds. Considering the local and long‐range dependencies, this paper proposes an efficient text detector, namely T‐Skeleton, to obtain more reliable segmentation detections. In the spirit of object skeletonization, we introduce the text instance skeleton highlighting the semantically significant structure (similar to the skeleton of a fish) to explicitly capture the long‐range dependencies of text instances. The key idea of T‐Skeleton is to calibrate the coarse text proposals by embedding text instance skeletons to separate crowd texts accurately and robustly. We further design a channel attention module to enlarge the performance margin between T‐Skeleton and the segmentation baseline. Experimental results on four publicly available datasets show the superiority of T‐Skeleton in handling long and curved texts. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks based remote sensing change detection has recently shown significant performance improvement. However, small region changes and global‐local features in high‐resolution remote sensing images are not fully explored. This paper introduces a hybrid U‐shaped and transformer network for change detection in high‐resolution remote sensing images. Specifically, a UNet++‐based backbone to facilitate feature learning across different scales. In addition, we introduce a transformer‐based feature fusion module for extracting long‐range dependencies, which can enhance the representation ability of the network. Furthermore, the introduced efficient channel attention mechanism can efficiently calibrate the feature representation and concentrate on more important feature information. Thanks to the above designs, the proposed method enjoys a strong ability to extract local and global features for remote sensing change detection. Extensive experimental results on different remote sensing images show that our method can achieve superior performance in comparison with state‐of‐the‐art change detection methods. [ABSTRACT FROM AUTHOR]

In recent years, the task of maritime emergency rescue has increased, while the cost of time for traditional methods of search and rescue is pretty long with poor effect subject to the constraints of the complex circumstances around the sea, the effective conditions, and the support capability. This paper applies deep learning and proposes a YOLOv5s‐SwinDS algorithm for target detection in distress at sea. Firstly, the backbone network of the YOLOv5s algorithm is replaced by swin transformer, and a multi‐level feature fusion module is introduced to enhance the feature expression ability for maritime targets. Secondly, deformable convolutional networks v2 (DCNv2) is used instead of traditional convolution to improve the recognition capability for irregular targets when the neck network features are output. Finally, the CIoU loss function is replaced with SIoU to reduce the redundant box effectively while accelerating the convergence and regression of the predicted box. Experimenting on the publicly dataset SeaDronesSee, the Precision$Precision$, Recall$Recall$, mAP0.5$mAP_{0.5}$ and mAP0.5−0.95$mAP_{0.5-0.95}$ of YOLOv5s‐SwinDS model are 87.9%, 75.8%, 79.1% and 42.9%, respectively, which get higher results than the original YOLOv5s model, the YOLOv7 series of models, and the YOLOv8 series of models. The experiments verifies that the algorithm has good performance in detecting maritime distress targets. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for hierarchical image segmentation based on the nonsymetry and anti‐packing pattern representation model (NAM) and the hierarchical density‐based spatial clustering of application with noise (HDBSCAN). The proposed framework consists of two phases. In the first phase, a super‐pixel generation algorithm base on NAM is proposed. In the second phase, instead of defining an affinity matrix to merge similar regions using spatial clustering, the distance matrix defined by different region features is directly fitted into an HDBSCAN clustering module in order to merge similar regions efficiently. Similar adjacent regions can be merged into larger ones progressively and form a segmentation dendrogram for image segmentation with the clustering module. The experiments show that the proposed algorithm has a comparable or even better performance compared to the state‐of‐the‐art hierarchical image segmentation algorithms while having much less time and memory consumption. [ABSTRACT FROM AUTHOR]

Computer vision‐based fire detection algorithms have been extensively researched over the past decades as they offer reliable and efficient solutions to early fire detection, as well as economical solutions in outdoor environments. The proposed systems have different levels of complexity; nevertheless, they rely heavily on colour based fire segmentation as the first processing stage using different colour spaces. This paper presents a new flame‐based colour space which accentuates the difference between fire and non‐fire pixel values. The hybrid Artificial Bee Colony‐Teaching Learning‐based Optimisation (ABC‐TLBO) and the k‐medoids clustering algorithm along with a Feature image constructed manually from a wide dataset of fire and non‐fire pixels were used to deduce the optimised colour space conversion matrix. The latter can then be used to transform the RGB pixels from a camera feed into the new Flame‐based colour space for a more efficient fire segmentation. The proposed colour space is capable to separate fire and non‐fire pixels into two intensity classes, whereby the resulting between‐class variance is significantly widened; this maximises the effectiveness of the binary thresholding to separate fire from non‐fire pixels. The experimental results demonstrate that the proposed flame‐based colour space provides a superior average qualitative and quantitative performance on benchmark datasets compared to state‐of‐the‐art colour models; a 20% average increase in F1 score demonstrates a better balance between fire detection and false alarm rates. Moreover, the computation time of the fire detection system is faster by 15.6% in average. The proposed system provides a better implementation of the colour‐based fire segmentation processing stage in vision‐based fire detection systems; it vindicates, in this application context, the superiority of flame‐tuned colour space over conventional ones. [ABSTRACT FROM AUTHOR]

Aircraft coating process has been an important part in manufacturing process of modern aviation products. For coating defect detection, the manual observation with naked eyes is usually utilized, which leads to low production efficiency. In this paper, the authors propose the improved YOLOv5‐OBB with the channel‐spatial attention block (CSAB), feature pyramid non‐local module (FPNM) and structured sparsity slimming criterion (SSSC). The CSAB can pay more attention to effective channel information features from the channel dimension and the target information area from the spatial dimension. The effective non‐local module called FPNM is proposed to further improve the detection accuracy. The authors utilize the oriented bounding boxes (OBB) to reduce redundant background information for coating defect detection. In addition, the SSSC is proposed to achieve network slimming and trade‐off between the efficiency and accuracy. The experimental results on several datasets demonstrate the effectiveness of the authors' scheme, which achieves superior performance. [ABSTRACT FROM AUTHOR]

Inverse halftoning is a technology that converts a binary image into a continuous tone image. Due to the wide application of inverse halftoning, many scholars have proposed several deep convolutional neural networks (DCNN) to optimize their performance. According to the observation, there is still room for improvement in content generation and detail recovery of the inverse halftone images generated by using the existing methods. Therefore, an inverse halftoning method based on supervised DCNN is proposed in this paper. The method consists of two parts: the multi‐level feature extraction model uses the down‐sampling to extract the features from the halftone image and remove the halftone noise dots on flat areas, which is implemented by four convolutional layers; the image reconstruction model uses up‐sampling to reconstruct image information, which is realized by four convolutional layers and two dense residual blocks. At the same time, in order to further recover the details, the down‐sampling feature maps and up‐sampling feature maps of the same size are concatenated by addition layers. Experimental results show that compared with other methods, the inverse halftone images obtained by the proposed network have better results in both subjective and objective evaluations. [ABSTRACT FROM AUTHOR]

Hazy images suffer from two problems. The low contrast can be enhanced by estimating a transmission layer, and the colour cast can be restored by estimating an airlight. These two variables, together with the albedo layer, are the constitutive elements of a hazy image. The resulting quality of dehazed images is inextricably linked to the accurate estimation of these components. However, it is ill‐posed to decompose these variables from a single image. As such, this paper presents an innovative algorithm intended to facilitate the optimal decomposition of a hazy image. Using the Markov random field model, an optimal framework is established that allows the simultaneous estimation of the three components across the three‐colour channels. To improve the visual quality, three improvements are proposed in the variational solution for the optimal components. The dehazed result is recomposed from the components with the transmission enhanced to circumvent any potential artefacts or information loss. Extensive experiments on natural images corroborate that the proposed algorithm outperforms state‐of‐the‐art dehazing methods, both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Image inpainting aims to repair the damaged region according to the known content in the damaged image. Recently, image inpainting methods have poor effects on high‐resolution damaged images, and the research on the inpainting of large‐area damaged images is limited. Therefore, this paper proposes an image inpainting method based on Generative Adversarial Networks (GAN) inversion and autoencoder. This work consists of two phases: first, the authors design an autoencoder‐based GAN, which learns the mapping from noise to low‐dimensional feature maps by training a generator, and then converts the generated feature maps into high‐resolution images. Thus, the difficulty of learning the mapping relationship is reduced. Second, the authors adopt the learning‐based GAN inversion to infer the closest latent code. The trained GAN is then used to reconstruct the complete image. Finally, the authors compare their method with other classical methods on the CelebAMask‐HQ, Flickr‐Faces‐HQ, and ImageNet datasets. According to the quantitative comparison, when the mask range is large, in other words, when the image has a large area of damage, the authors' method is superior to the comparison methods. According to the qualitative comparison, the structure of the high‐resolution image inpainted by the authors' method is more reasonable and the texture details are more realistic. [ABSTRACT FROM AUTHOR]

Diffusion Tensor Imaging (DTI) is a new technique to characterize the brain structure. It is a special modality for Magnetic Resonance (MRI) which provides the information of fiber orientation. By quantitatively comparing the difference of the fiber information of different image pairs, one can effectively track the status of the nerve diseases. For this purpose, it is necessary to deform the DTI image of the patient (floating image) to the same shape of the template image (target image). This is so called DTI registration, which is usually formulated by a 3D variational problem. However, the numerical implementation for 3D variational problem costs too much CPU consumption. To reduce CPU consumption, it is meaningful to give some research on the structure of the solution for DTI registration. In this paper, the structure of the solution for DTI registration is mainly focused on. For this purpose, an explicit representation of the solution for a DTI registration model is presented. Based on this representation, a new DTI registration algorithm is induced. Several numerical experiments are also performed to show the efficiency of the proposed algorithm in DTI registration. [ABSTRACT FROM AUTHOR]

The bridge bottom crack detection provides important state information for bridge disease control and safety assessment. This paper proposes a detection method based on deep learning Faster R‐CNN and BIM (Building Information Modeling). The UAV (Unmanned Aerial Vehicle) was used for close aerial photography to obtain high‐resolution crack images of the concrete surface at the bottom of a bridge. Through deep learning algorithms, a Faster R‐CNN model is trained and established for crack identifications. The crack identification accuracy rate and recall rate reach 92.03% and 96.54%, respectively. Crack images are mapped to a BIM model developed for the chosen bridge, and the box girder family with cracks and the three crack families of transverse cracks, longitudinal cracks and turtle cracks are established. The cracks are located and the visualization of the beam bridge with cracks was completed. In order to better assess the health condition of the bridge. The results show that the combination of UAV bridge crack detection and modelling solves the remote, visual and automated detection of cracks on the surface of bridge structures, which are difficult to reach manually, and has important scientific research and engineering application value. [ABSTRACT FROM AUTHOR]

When using untrusted third parties to compress and transmit images in real‐life scenarios, it is vital to encrypt them before compression. In order to better address the issues of low security in the original image and poor reconstruction quality of the encrypted image during compressed sensing, this paper proposes a 2D compressed sensing scheme for encrypted images based on complex‐valued measurement matrix (2DCS‐CVM). Firstly, the SHA‐256 algorithm generates keys for the hyperchaotic Lorenz system, and then the chaotic sequences are used to create encrypted images with increased security through subtractive diffusion and global permutation. Secondly, the complex‐valued Vandermonde measurement matrix is utilized for 2D compressed sensing on the encrypted image, and the two‐dimensional projected gradient with embedding decryption algorithm is used to generate recovered images with improved reconstruction performance. Finally, the measurement matrix's computational complexity and transmission bandwidth are reduced through structural sparsification with sparse random matrices. Simulation results demonstrate that this scheme offers an optimal balance between storage, computational complexity, hardware implementation, and reconstruction performance while providing excellent security and robustness. [ABSTRACT FROM AUTHOR]

Image inpainting networks based on deep learning techniques have been widely used in many important fields. However, most inpainting networks fail to generate desirable repaired images. This may be due to their failure to extract effective features and accurately assign high weights to the undamaged regions. To alleviate these problems, an image inpainting network based on gated convolution and multi‐level attention mechanism (IIN‐GCMAM) is proposed in this paper. This network follows encoder–decoder architecture, consisting of the gated convolution encoder (GC‐encoder) and the multi‐level attention mechanism decoder (MAM‐decoder). The GC‐encoder weighs the extracted features with gated convolutions, which reduces the interference caused by the damaged regions. The multi‐level attention mechanism employed in the MAM‐decoder uses multi‐scale feature maps spatially and channel‐wise to improve the consistency in global structure and the fineness of repaired results. Extensive experiments are conducted on the common datasets, Paris StreetView and CelebA. Experimental results indicate that the proposed IIN‐GCMAM can achieve a good performance on the common evaluation metrics and visual effects. It can achieve 0.0408, 0.720, and 22.27 in MAE, SSIM, and PSNR at the mask ratio of 50%–60%, respectively. [ABSTRACT FROM AUTHOR]

License plate detection is an important task in Intelligent Transportation Systems (ITS) and has a wide range of applications in vehicle management, traffic control, and public safety. In order to improve the accuracy and speed of mobile recognition, an improved lightweight YOLOv5s model is proposed for license plate detection. First, an improved Stemblock network is used to replace the original Focus layer in the network, which ensures strong feature expression capability and reduces a large number of parameters to lower the computational complexity; then, an improved lightweight network, ShuffleNetv2, is used to replace the backbone network of the YOLOv5s, which makes the model lighter and ensures the detection accuracy at the same time. Then, a feature enhancement module is designed to reduce the information loss caused by the rearrangement of the backbone network channels, which facilitates the information interaction in the feature fusion process; finally, the low‐, medium‐ and high‐level features in the Shufflenetv2 network structure are fused to form the final high‐level output features. Experimental results on the CCPD dataset show that compared to other methods this paper obtains better performance and faster speed in the license plate detection task, in which the average precision mean value reaches 96.6%, and can achieve a detection speed of 43.86 frame/s, and the parameter volume is reduced to 5.07 M. [ABSTRACT FROM AUTHOR]

To develop a fast and effective image deblurring method, the blind recovery of motion‐blurred images based on DeblurGAN(GAN, Generative Adversarial Networks) is researched.Firstly, the number of residual modules in the DeblurGAN network is changed, and an attempt is made to optimize the network structure to achieve better results in the blind recovery of motion‐blurred images. Secondly, an image deblurring method based on the improved DeblurGAN network is proposed.This paper makes corresponding adjustments to the generator and discriminator networks to change their input and output size to 512 × 512 while keeping the overall structure of the network unchanged, and the experimental results show that the quality of the restored images has been greatly improved. In addition, the images were divided into four classes of images,to achieve improved restoration of blurred images with specific content. Experimental results on the benchmark GoPro dataset validate that the improved DeblurGAN achieves more than 1.5 dB improvement than DeblurGAN in terms of peak signal‐to‐noise ratio (PSNR) as trained utilizing the same amount of data, and structural similarity assessment (SSIM) evaluation means and maximum values increased between 0.2 and 0.3. The improved DeblurGAN is more effective in terms of both blur removal and detail recovery. [ABSTRACT FROM AUTHOR]

Convolution and matrix operations are both important computations in Deep Neural Networks (DNNs). However, the significant differences between convolution and matrix computation patterns have posed a challenge in efficiently supporting both convolution (Conv) and general matrix multiplication (GEMM) on hardware design. This paper proposes a Conv‐GEMM reconfigurable accelerator architecture for high throughput edge processing. A weight stationary‐row streaming (WS‐RS) dataflow scheme is proposed, which maximizes data reuse through hierarchical memory structures and flexible PE connections, and supports high throughput edge‐based deep learning algorithms. Based on the proposed dataflow, multi‐scale memory access network (MMAN), reconfigurable accumulator array (RAA), and configurable instruction set architecture (ISA) are designed to optimize computation throughput and energy efficiency. The accelerator is designed under 65 nm technology, achieves peak performance of 1.15 TOPS at 250 MHz, with an energy efficiency of 1.14 TOPS/W. The GEMM computation achieves 85.7% latency improvement and the Mobilenet‐V1 processing achieves a throughput of 529 fps under a 256 × 224 image size and an 87.15% (top‐5) accuracy on the ImageNet dataset. [ABSTRACT FROM AUTHOR]

This paper proposes a robust watermarking method against high‐density salt and pepper noise attacks. Automatic region of interest (ROI) detection, embedding encoded data, removing different densities of noise, data extraction by omitting, and labeling the noisy pixels of Region of Non‐Interest (RONI), and decoding the extracted data using ROI pixel information as a key, are various steps of the presented scheme. The automatic ROI detection method separates the RONI from ROI with four vertices of the smallest rectangle, for the embedding process. The encoded watermark data is embedded into the least significant bits of RONI in four neighbour pixels. Adaptive Removal of high‐density Salt and pepper Noise method can enhance image quality and reduce the effect of salt and pepper noise attacks. The embedded information is preserved from destruction if the host image is impaired through the power of robustness. The best results are obtained through the action of extracting the watermark from RONI pixels, utilizing the same ROI detection method. Omitting and labeling the noisy pixels of the RONI will ensure healthy extracted watermark data, leading to decreased Bit Error Rate (BER) values. Finally, these data are interpreted using the key of ROI pixels, and the watermark data is decoded and retrieved. Due to salt and pepper noise obliterating pixel bits and their corresponding transform coefficients in the transform domain, the spatial domain is employed to enhance robustness against such attacks. The results show the high performance of the presented scheme. The average BER value for five MRI databases in a 97% salt and pepper noise attack is 38.6. [ABSTRACT FROM AUTHOR]