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As an important supporting material for calligraphy and painting relics in collections, the silk substrate is generally in acidification. And owing to an extremely high light response, it is very prone to different forms of lighting damage, including color and mechanical aspects. Considering that the Light Emitting Diode (LED) with flexible spectral compositions is widely used in the lighting environment of collections, the spectral responsivity of acidified silk substrates is the core issue concerning the long-term conservation of calligraphy and painting relics. In this study, based on the accelerated aging experiment of nine narrowband LEDs with different wavelengths (λ) in the visible range on silk samples with different acidification degrees (pH values), the infrared spectrum was measured non periodically. Processed with the Principal Component Analysis (PCA), the obtained ΔPC1 was used to evaluate the lighting damage of silk samples. Based on the damage change curve, its spectral responsivity function, that is, the mathematical relationship between lighting damage and λ and pH values, was further fitted. Combined with the previous research on the spectral responsivity of colorants, an evaluation method for lighting damage was proposed, which is the key to the conservation of calligraphy and painting relics. [ABSTRACT FROM AUTHOR]

In recent years, many deep learning-based object detection methods have performed well in various applications, especially in large-scale object detection. However, when detecting small targets, previous object detection algorithms cannot achieve good results due to the characteristics of the small targets themselves. To address the aforementioned issues, we propose the small object algorithm model MCF-YOLOv5, which has undergone three improvements based on YOLOv5. Firstly, a data augmentation strategy combining Mixup and Mosaic is used to increase the number of small targets in the image and reduce the interference of noise and changes in detection. Secondly, in order to accurately locate the position of small targets and reduce the impact of unimportant information on small targets in the image, the attention mechanism coordinate attention is introduced in YOLOv5's neck network. Finally, we improve the Feature Pyramid Network (FPN) structure and add a small object detection layer to enhance the feature extraction ability of small objects and improve the detection accuracy of small objects. The experimental results show that, with a small increase in computational complexity, the proposed MCF-YOLOv5 achieves better performance than the baseline on both the VisDrone2021 dataset and the Tsinghua Tencent100K dataset. Compared with YOLOv5, MCF-YOLOv5 has improved detection APsmall by 3.3% and 3.6%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we propose a cavity-coupled perfect absorber (CCPA) consisting of a bottom gold layer, a SiO2 layer, and multilayer MIM (metal–insulator-metal) nanowires. By coupling the Fabry-Pérot (F-P) cavity mode with the magnetic plasmons (MPs) resonance mode in multilayer MIM structure, the MPs resonance in the multilayer MIM structure is enhanced and leads to increased absorption of the structure. By designing and optimizing the parameters, near-perfect absorption in the mid-infrared wavelengths can be achieved. The positions of the two absorption peaks can be adjusted by changing the width of the nanowires and the thickness of the SiO2 layer, resulting in the formant excitation at any position in the mid-infrared. The full-width at half-maximum (FWHM) of the short-wave peak is only 0.076 µm, and the FWHM of the long-wave peak is only 0.46 µm. The long-wave absorption peak maintains high efficiency and stability even at large incident angles, exhibiting “omnidirectional” characteristics. The tunable mid-infrared emission peak can be matched to the characteristic spectra of gases, making it suitable for infrared radiation sources in gas detection. [ABSTRACT FROM AUTHOR]

[Display omitted] • Pt 0.9 Pd 0.1 -TiO 2 can efficiently degrade MC-LR under various condition. • Tiny amounts of Pd in PtPd alloy regulates electronic localization of Pt 0.9 Pd 0.1 -TiO 2. • Generation of bountiful hydroxyl radicals by overcoming low energy barrier is realized by Pt 0.9 Pd 0.1 -TiO 2. • The exciton lifetime is shortened from 2.51 ns on pristine TiO 2 to 1.16 ns on Pt 0.9 Pd 0.1 -TiO 2. • Pt 0.9 Pd 0.1 alloy nanoparticles notably strengthens the surface hydroxylation of TiO 2. The metal co-catalyst-based photocatalytic oxidation technology provides a promising approach to eliminate contaminants from water. However, the applicability of these catalysts often suffer from severe carrier recombination and high activation energy barriers of reactive oxygen species (ROS). Herein, self-supporting TiO 2 nanotube arrays were engineered with PtPd atom clusters having atomically controlled molar ratio, assembling a dynamic Mott-Schottky catalyst for photocatalytic removal of Microcystin-LR, a toxin produced by cyanobacteria. The separation of photogenerated carriers and the creation of reactive oxygen species (ROS) are optimized by regulating electronic construction via tailoring the Pd ratio in PtPd atom clusters (ca. 4.6 nm in size). The exciton lifetime of the optimum Pt 0.9 Pd 0.1 -TiO 2 is shortened from 2.51 ns on pure TiO 2 to 1.16 ns, and the Gibbs free energy for forming hydroxyl radicals (•OH) varies from −0.826 eV (pure TiO 2) to −2.576 eV, powering plentiful •OH when Pt 0.9 Pd 0.1 -TiO 2 illuminated under solar lights. The kinetic constant of Pt 0.9 Pd 0.1 -TiO 2 for MC-LR removal is 12.7 times higher than that of the pristine TiO 2 , presenting exceptional photocatalytic capacity in turning Microcystin-LR into harmless chemicals. [ABSTRACT FROM AUTHOR]

Spatial rich model (SRM) steganalysis feature is formed by high-order statistics collected from image noise residuals. These statistics are simply rescaled before machine learning. It is noted that SRM features of different cover images are very different. In this paper, we propose a feature normalization method based on random feature-subsets (NRS) for SRM. We randomly draw feature-subsets from SRM feature. Then these feature-subsets are normalized by using per-sample rescaling method to make the feature-subsets of different images have the same 1-norm (the sum of all elements). The proposed NRS method can adjust the feature distribution and increase the feature diversity. The normalized feature-subsets can achieve better detection performance and also can be used as a useful complement for the existing steganalysis features. Experimental results show that: (1) a small amount of normalized feature-subset supplement can obviously improve the detection performance of SRM feature; (2) under the same dimensionality, the proposed NRS version feature can achieve a better detection accuracy than that of original feature; (3) the proposed NRS method is applicable to projections of spatial rich model feature; (4) compared with steganalysis feature extraction, the computational time of NRS can be negligible. [ABSTRACT FROM AUTHOR]

Spatial rich model (SRM) is a classic steganalysis method, which collects high-order co-occurrences from truncated noise residuals as feature to capture the local-range dependencies of an image. Increasing the truncation threshold and the co-occurrence order will lead to a higher-dimensional feature, which can exploit more statistical bins and capture dependencies across larger-range neighborhood, but this will suffer from the curse of dimensionality. In this paper, we propose a fast projection method to increase the statistical robustness of the higher-dimensional SRM feature while decreasing its dimensionality. The proposed projection method is applicable to co-occurrence-based steganalysis features. The detection performance and the computational complexity of the proposed method are investigated on three content-adaptive steganographic algorithms in spatial domain. [ABSTRACT FROM AUTHOR]

[Display omitted] • Co 3 O 4 nanopartices transform into nanoflakes driven by an electric field. • Morphologically transformed Co 3 O 4 boosts the high yield of atomic H* and •OH. • Co 3 O 4 cathode electrochemically degrades Thiamphenicol in wide pH window. • Co 3 O 4 cathode is feasible and ultra-stable in dealing with actual water. Degradation of halogenated organic compounds using the electro-reduction process is a promising approach for advanced water treatment. In this work, antiferromagnetic Co 3 O 4 nanoparticles were first loaded on a uniform anodic porous TiO 2 surface through a hydrothermal strategy, then subjected to an electrochemical reduction, which transform from nanoparticle-shape to interconnected nanoflakes. Compared with the pristine Co 3 O 4 nanoparticles, the morphologically transformed Co 3 O 4 cathode features a porous interface with amorphous Co(OH) 2 and CoOOH shell, which favors the generation of abundant •OH radicals along with lower overpotential for reducing H+ to H*. On account of the plentiful radicals and self-supporting nature, the resulting Co 3 O 4 cathode retains high activity for efficiently degrading Thiamphenicol in water, even after 10 h run, demonstrating remarkable durability. Moreover, it is adaptable in a wide pH window of 3 to 10, and effectual when the initial Thiamphenicol concentration increases from 0.5 to 50 mg/L regardless of the water background (tap water, lake water, or river water). This study delivers a profound cognition about the dynamic behaviors of Co 3 O 4 -based electrocatalysts applied in eliminating halogenated antibiotics. [ABSTRACT FROM AUTHOR]

For compressive sensing magnetic resonance (CS-MR) image reconstruction, it is vital to preserve edges and textures while eliminating aliasing artifacts. The total variation (TV) regularization preserves edges well but causes staircase effect and fails to preserve textures. The high-order regularization can eliminate the staircase effect but causes edge blurring. The nonlocal TV (NLTV) regularization can preserve textures well, but causes extra artifacts and is likely to preserve the texture-like artificial structures caused by aliasing artifacts mistakenly. In this paper, we assume that the image consists of cartoon component and anisotropic component. For the cartoon component, we utilize the fractional-order TV regularization to eliminate the staircase effect and avoid edge blurring, and more importantly, we can avoid its disadvantage in texture preservation because there are no textures in the cartoon component. We utilize the NLTV regularization and the shearlet based sparsity regularization for the anisotropic component with piecewise-constant background. Without the effect of intensity inhomogeneity, the NLTV regularization can avoid preserving the texture-like artificial structures while preserving the real edges. The shearlet based sparsity regularization can provide further improvement of the image quality. Numerical experiments demonstrate that our method can eliminate the aliasing artifacts and preserve the edges and textures efficiently. [ABSTRACT FROM AUTHOR]

Very high resolution (VHR) images change detection plays an important role in many remote sensing applications, such as military reconnaissance, urban planning and natural resource monitoring. Recently, fully connected conditional random field (FCCRF)-facilitated deep convolutional neural networks have shown promising results in change detection. However, the FCCRF in change detection currently is still postprocessing based on the output of the front-end network, which is not a convenient end-to-end network model and cannot combine front-end network knowledge with the knowledge of pairwise potential. Therefore, we propose a new end-to-end deep Siamese pairwise potential CRFs network (PPNet) for VHR images change detection. Specifically, this method adds a conditional random field recurrent neural network (CRF-RNN) unit into the convolutional neural network and integrates the knowledge of unary potential and pairwise potential in the end-to-end training process, aiming to refine the edges of changed areas and to remove the distant noise. In order to correct the front-end network identification errors, the method uses effective channel attention (ECA) to further effectively distinguish the change areas. Our experimental results on two data sets verify that the proposed method has more advanced capability with almost no increase in the number of parameters and effectively avoids the overfitting phenomenon in the training process. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we introduce a simple reweighted residual-feedback iterative (RRFI) algorithm which provides a general framework to solve the fractional-order total variation regularized models with different fidelity terms. We provide a sufficient condition for the convergence of this algorithm. As an application, we use this algorithm to solve the TV and fractional-order TV regularized models with two special fidelity terms for multiplicative noise removal of partly-textured images. To improve the performance, we define gradually varying fuzzy membership degrees to mark the possibilities of a pixel belonging to edges, textured regions and flat regions. Using the fuzzy membership degrees, we add local behavior to the choice of the parameters and the updating of the weighting matrix, and then propose an adaptive RRFI algorithm for multiplicative noise removal. Numerical results show that the RRFI algorithm has low computational cost and fast convergence speed. The adaptive RRFI algorithm performs well for preserving details and eliminating the staircase effect while removing noise, and therefore can improve the result visually efficiently. [Copyright &y& Elsevier]

Abstract: In this paper we developed an iteratively approximated gradient projection algorithm for -minimization problems arising from sparse signal reconstruction in compressive sensing. By introducing a relaxed variable, the noisy problem can be transformed into the problem with equality constraints. The nonsmooth term was tackled by variable-splitting techniques. Thus the problem was transformed into a quadratic programming problem. All linear variables in the objective function were imposed on regularization. Based on ideas of quasi-Lagrangian functions and partial duality, a reduced quadratic programming problem can be obtained iteratively. At each iteration, we applied gradient projection methods with approximated gradients to get the next iterates. The computational experiments show the proposed method is very effective. [Copyright &y& Elsevier]

Proposes a wavelet-based image coding scheme which exploits jointly the statistical redundancy and the irrelevance properties of human visual system. Wavelet decomposition and reconstruction image; Visibility threshold model; Visual compressor and expander; Simulation and results.

Hyperspectral image (HSI) classification accuracy has been greatly improved by employing deep learning. The current research mainly focuses on how to build a deep network to improve the accuracy. However, these networks tend to be more complex and have more parameters, which makes the model difficult to train and easy to overfit. Therefore, we present a lightweight deep convolutional neural network (CNN) model called S2FEF-CNN. In this model, three S2FEF blocks are used for the joint spectral–spatial features extraction. Each S2FEF block uses 1D spectral convolution to extract spectral features and 2D spatial convolution to extract spatial features, respectively, and then fuses spectral and spatial features by multiplication. Instead of using the full connected layer, two pooling layers follow three blocks for dimension reduction, which further reduces the training parameters. We compared our method with some state-of-the-art HSI classification methods based on deep network on three commonly used hyperspectral datasets. The results show that our network can achieve a comparable classification accuracy with significantly reduced parameters compared to the above deep networks, which reflects its potential advantages in HSI classification. [ABSTRACT FROM AUTHOR]

Weakly supervised object detection (WSOD) aims to predict a set of bounding boxes and corresponding category labels for instances with only image-level supervisions. Compared with fully supervised object detection, WSOD in remote sensing images (RSIs) is much more challenging due to the vast foreground-related context regions. In this paper, we propose a progressive image-level and instance-level feature refinement network to address the problems of missing detection and part domination for WSOD in RSIs. Firstly, we propose a multi-label attention mining loss (MAML)-guided image-level feature refinement branch to effectively allocate the computational resources towards the most informative part of images. With the supervision of MAML, all latent instances in images are emphasized. However, image-level feature refinement further expands responsive gaps between the informative part and other sub-optimal informative ones, which results in exacerbating the problem of part domination. In order to alleviate the above-mentioned limitation, we further construct an instance-level feature refinement branch to re-balance the contributions of different adjacent candidate bounding boxes according to the detection task. An instance selection loss (ISL) is proposed to progressively boost the representation of salient regions by exploring supervision from the network itself. Finally, we integrate the image-level and instance-level feature refinement branches into a complete network and the proposed MAML and ISL functions are merged with class classification and box regression to optimize the whole WSOD network in an end-to-end training fashion. We conduct experiments on two popular WSOD datasets, NWPU VHR-10.v2 and DIOR. All the experimental results demonstrate that our method achieves a competitive performance compared with other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Due to the delay in the row-wise exposure and the lack of stable support when a photographer holds a CMOS camera, video jitter and rolling shutter distortion are closely coupled degradations in the captured videos. However, previous methods have rarely considered both phenomena and usually treat them separately, with stabilization approaches that are unable to handle the rolling shutter effect and rolling shutter removal algorithms that are incapable of addressing motion shake. To tackle this problem, we propose a novel method that simultaneously stabilizes and rectifies a rolling shutter shaky video. The key issue is to estimate both inter-frame motion and intra-frame motion. Specifically, for each pair of adjacent frames, we first estimate a set of spatially variant inter-frame motions using a neighbor-motion-aware local motion model, where the classical mesh-based model is improved by introducing a new constraint to enhance the neighbor motion consistency. Then, different from other 2D rolling shutter removal methods that assume the pixels in the same row have a single intra-frame motion, we build a novel mesh-based intra-frame motion calculation model to cope with the depth variation in a mesh row and obtain more faithful estimation results. Finally, temporal and spatial motion constraints and an adaptive weight assignment strategy are considered together to generate the optimal warping transformations for different motion situations. Experimental results demonstrate the effectiveness and superiority of the proposed method when compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

We present a dual-layer hafnium dioxide (HfO2) grating capable of full-color modulation in the visible spectrum by leveraging the magnetic dipole resonance induced by the lower-layer HfO2 grating, while the upper-layer HfO2 grating serves as a refractive index matching layer to effectively suppress high-order Mie resonances at shorter wavelengths. The HfO2/HfO2 grating exhibits a significantly larger distribution area in the CIE 1931 chromaticity diagram compared to the HfO2 grating. Furthermore, the structural color saturation closely approximates that of monochromatic light. Under varying background refractive index environments, this structure consistently exhibits high-quality structural color. However, the hue of the structural color undergoes alterations. When the polarization angle is below 20°, the saturation of the acquired structural color remains remarkably consistent. However, exceeding 20° results in a significant degradation in the quality of the structural color. This study demonstrates the promising potential for diverse applications, encompassing fields such as imaging and displays. [ABSTRACT FROM AUTHOR]

Due to the constrained processing capabilities of real-time detection techniques in remote sensing applications, it is often difficult to obtain detection results with high accuracy in practice. To address this problem, we introduce a new real-time anomaly detection algorithm for hyperspectral images called cloud–edge RX (CE-RX). The algorithm combines the advantages of cloud and edge computing. During the data acquisition process, the edge performs real-time detection on the data just captured to obtain a coarse result and find the suspicious anomalies. At regular intervals, the suspicious anomalies are sent to the cloud for further detection with a highly accurate algorithm, then the cloud sends back the (high-accuracy) results to the edge for information updating. After receiving the results from the cloud, the edge updates the information of the detector in the real-time algorithm to improve the detection accuracy of the next acquired piece of data. Our experimental results demonstrate that the proposed cloud–edge collaborative algorithm can obtain more accurate results than existing real-time detection algorithms. [ABSTRACT FROM AUTHOR]

Spiking Neural Network (SNN) has been recognized as the third generation of neural networks. Conventionally, a SNN can be converted from a pre-trained Artificial Neural Network (ANN) with less computation and memory than training from scratch. But, these converted SNNs are vulnerable to adversarial attacks. Numerical experiments demonstrate that the SNN trained by optimizing the loss function will be more adversarial robust, but the theoretical analysis for the mechanism of robustness is lacking. In this paper, we provide a theoretical explanation by analyzing the expected risk function. Starting by modeling the stochastic process introduced by the Poisson encoder, we prove that there is a positive semidefinite regularizer. Perhaps surprisingly, this regularizer can make the gradients of the output with respect to input closer to zero, thus resulting in inherent robustness against adversarial attacks. Extensive experiments on the CIFAR10 and CIFAR100 datasets support our point of view. For example, we find that the sum of squares of the gradients of the converted SNNs is 13 ∼ 160 times that of the trained SNNs. And, the smaller the sum of the squares of the gradients, the smaller the degradation of accuracy under adversarial attack. • The adversarial robustness of SNNs based on rate encoder is explained mathematically. • A regularizer distinguishes the directly trained SNN from the converted SNN. • The coding length can also change the gradient of the input to affect the robustness. • We compare more types of the trained SNN with the converted SNN. [ABSTRACT FROM AUTHOR]

The article discusses systematic review of studies to summarize the effects of yoga in people with dementia. It mentions a study revealing a yoga group lowered blood pressure, reduced body flexibility improved muscle strength and endurance; and another study revealing that yoga improved immediate and delayed recall of verbal (RAVLT) and visual memory (CFT). It adds benefits of yoga in people with dementia.

Dynamic stochastic resonance (DSR) is a distinctive technique for enhancement of dark and low-contrast image. Noise is necessary for DSR based image enhancement and the level of noise will be enlarged simultaneously with brightness, which reduces the perceptual quality of the enhanced image greatly and also increases the difficulty of subsequent denoising because removing high level of noise often leads to serious loss of image details. In this paper, instead of removing noise after the enhancement process is complete, we propose to suppress noise gradually and simultaneously in the process of enhancement. We rewrite the traditional partial differential equation (PDE) based DSR model in variational framework firstly, and then propose a novel total variation regularized (TV) DSR method for image enhancement. The existence and uniqueness of solution of the TV regularized DSR model is proved theoretically. Moreover, we generalize the TV regularized DSR model in variational framework and in PDE framework, respectively, and therefore we can incorporate more existing denoising methods into our approach. Numerical comparisons demonstrate that the proposed technique gives significant performance in terms of contrast and brightness enhancement as well as noise suppression, and therefore can obtain enhanced image with good perceptual quality. [ABSTRACT FROM AUTHOR]

Photoelectrocatalysis (PEC) is an effective approach to eliminate carcinogen hexavalent chromium (Cr(VI)) in wastewater, in which high-performance catalysts are crucial. Herein, controlled growth of thin molybdenum disulphide (MoS 2) nanosheets on self-supported tungsten trioxide (WO 3) created an all-solid-state MoS 2 /WO 3 heterojunction serving as electrode and catalyst simultaneously for removing Cr(VI). Countless small and thin MoS 2 nanoflakes build in a huge and porous interface for harvesting lights and adsorbing chromium species. And the highly conductive WO 3 substrate facilitates the transfer of those photoexcited-electron and therefore suppresses the recombination between electrons and holes. Furthermore, assisted by bias potential, electron streams from external circuit render an electron-rich interface at the MoS 2 /WO 3 cathode, accelerating the Cr(VI) reduction by PEC. At −1.2 V, the PEC reduction efficiency of Cr(VI) reaches 100% within 30 min, surpassing the pristine WO 3 by 2.7 times. The generated Cr(III) ions can be immobilized on the porous MoS 2 /WO 3 cathode through electrostatic attraction, enabling removal of total chromium. More importantly, the Cr(III) anchored to the catalyst can be effortlessly recovered by eluting with clean water, which also refreshes the MoS 2 /WO 3 cathode. This study provides a new approach to fabricating photoelectrodes for sustainable PEC reduction and treatment of Cr(VI) containing wastewater. [ABSTRACT FROM AUTHOR]

Anomaly detection plays an important role in remotely sensed hyperspectral image (HSI) processing. Recently, compressive sensing technology has been widely used in hyperspectral imaging. However, the reconstruction from compressive HSI and detection are commonly completed independently, which will reduce the processing’s efficiency and accuracy. In this paper, we propose a framework for hyperspectral compressive sensing with anomaly detection which reconstruct the HSI and detect the anomalies simultaneously. In the proposed method, the HSI is composed of the background and anomaly parts in the tensor robust principal component analysis model. To characterize the low-dimensional structure of the background, a novel tensor nuclear norm is used to constrain the background tensor. As the anomaly part is formed by a few anomalous spectra, the anomaly part is assumed to be a tuber-wise sparse tensor. In addition, to enhance the separation of the background and anomaly, we minimize the sum of Mahalanobis distance of the background pixels. Experiments on four HSIs demonstrate that the proposed method outperforms several state-of-the-art methods on both reconstruction and anomaly detection accuracies. [ABSTRACT FROM AUTHOR]

The low spatial resolution of hyperspectral images means that existing mixed pixels rely heavily on spectral information, making it difficult to differentiate between the target of interest and the background. The endmember extraction method is powerful in enhancing spatial structure information for hyperspectral anomaly detection, whereas most approaches are based on matrix representation, which inevitably destroys the spatial or spectral information. In this paper, we treated the hyperspectral image as a third-order tensor and proposed a novel anomaly detection method based on a low-rank linear mixing model of the scene background. The obtained abundance maps possessed more distinctive features than the raw data, which was beneficial for identifying anomalies in the background. Specifically, the low-rank tensor background was approximated as the mode-3 product of an abundance tensor and endmember matrix. Due to the distinctive features of the background's abundance maps, we characterized them by tensor regularization and imposed low rankness through CP decomposition, smoothness, and sparsity. In addition, we utilized the ℓ 1 , 1 , 2 -norm to characterize the tube-wise sparsity of the anomaly, since it accounted for a small portion of the scene. The experimental results obtained using five real datasets demonstrated the outstanding performance of our proposed method. [ABSTRACT FROM AUTHOR]

Recently, residual and dense networks have effectively promoted the development of image super‐resolution (SR). However, most dense networks based SR methods do not make full use of dense feature information. To solve this problem, a pyramidal dense attention network for single image super‐resolution is proposed in this paper. In this method, the proposed pyramidal dense learning can gradually increase the width of the densely connected layer inside a pyramidal dense block to extract deep features efficiently. Meanwhile, the adaptive group convolution that the number of groups grows linearly with dense convolutional layers is introduced to relieve the parameter explosion. Besides, a novel joint attention to capture cross‐dimension interaction between the spatial dimensions and channel dimension in an efficient way for providing rich discriminative feature representations is also proposed. Extensive experimental results show that the method achieves comparable performance in comparison with the state‐of‐the‐art SR methods. [ABSTRACT FROM AUTHOR]

Fine-grained visual classification (FGVC) has small inter-class variations and large intra-class variations, therefore, recognizing sub-classes belonging to the same meta-class is a difficult task. Recent studies have primarily addressed this problem by locating the most discriminative image regions, and the extracted image regions have been used to improve the ability to capture subtle differences. Most of these studies used regular anchors to extract local features. However, the local features of the target are mostly irregular geometric shapes. These methods cannot fully extract the features and inevitably include a large amount of irrelevant information, resulting in reduced credibility of the evaluation results. However, the spatial relationship between the features is easily overlooked. This study proposes a novel local feature extraction anchor generator (LFEAG) to simulate the shapes of irregular features. Thus, discriminative features can be fully included in the extracted features. In addition, an effective symmetrized local feature extraction module (SLFEM) based on an attention mechanism is proposed to fully use the spatial relationship between the extracted local features and highlight discriminative features. Experiments on six popular fine-grained benchmark datasets: CUB-200-2011, Stanford Dogs, Food-101, Oxford-IIIT Pets, Aircraft and NA-Birds, are conducted to demonstrate the advantages of our proposed method. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) super-resolution aims at improving the spatial resolution of HSI by fusing a high spatial resolution multispectral image (MSI). To preserve local submanifold structures in HSI super-resolution, a novel superpixel graph-based super-resolution method is proposed. Firstly, the MSI is segmented into superpixel blocks to form two-directional feature tensors, then two graphs are created using spectral–spatial distance between the unfolded feature tensors. Secondly, two graph Laplacian terms involving underlying BTD factors of high-resolution HSI are developed, which ensures the inheritance of the spatial geometric structures. Finally, by incorporating graph Laplacian priors with the coupled BTD degradation model, a HSI super-resolution model is established. Experimental results demonstrate that the proposed method achieves better fused results compared with other advanced super-resolution methods, especially on the improvement of the spatial structure. [ABSTRACT FROM AUTHOR]

Object detection in remote sensing images (RSIs) poses great difficulties due to arbitrary orientations, various scales, and dense location of the targets over the ground. Recent evidence suggests that encoding the orientation information is of great use for training an accurate object detector for oriented object detection (OOD). In this article, we propose a new frequency-domain orientation learning (FDOL) module with two main components: the frequency-domain feature extraction (FFE) network and an orientation enhanced self-attention layer (OES-Layer). The FFE network models the interactions among spatial locations in the frequency domain to determine the frequency of spatial features. Then, these features are fed into our OES-Layer to learn the orientation information. Moreover, the orientation weights are adopted to guide the feature selection in a self-attention (SA) architecture, using them as a control gate to emphasize the spatial responses of target instances. Considering that the original similarity weights (calculated by the SA algorithm) do not distinctly model the orientation variation, the considered orientation weights provide an efficient asset to emphasize the orientation of objects. Extensive experiments on the DOTA and HRSC2016 datasets demonstrate that our method achieves state-of-the-art performance among single-scale methods while achieving competitive performance over multiscale methods. [ABSTRACT FROM AUTHOR]

In recent years, deblurring image with Poisson noise has attracted more and more attention in many areas such as astronomy and biological imaging. This is an ill-posed problem and can be regularized to improve the quality of the solution. Fractional-order total variation regularization can eliminate the staircase effect caused by the total variation regularization and avoid over-smoothing at the edges caused by the high-order total variation regularization, but it cannot preserve textures well. Non-local regularization can preserve textures well but often causes extra artifacts especially in the non-texture regions. In this paper, we deal with the cartoon component and the texture component differently and propose a cartoon-texture composite regularization based non-blind deblurring method. We utilize fractional-order total variation regularization for the cartoon component to eliminate the staircase effect and avoid over-smoothing at the edges, and use the non-local total variation regularization for the texture component to preserve the details and eliminate the artifacts in the non-textured regions. We develop an alternating direction method of multipliers based algorithm to solve the proposed model. Experiments results show that our method can improve the result both visually and in terms of the peak signal to noise ratio and structural similarity index efficiently. [ABSTRACT FROM AUTHOR]

Very-high-resolution (VHR) bi-temporal images change detection (CD) is a basic remote sensing images (RSIs) processing task. Recently, deep convolutional neural networks (DCNNs) have shown great feature representation abilities in computer vision tasks and have achieved remarkable breakthroughs in automatic CD. However, a great majority of the existing fusion-based CD methods pay no attention to the definition of CD, so they can only detect one-way changes. Therefore, we propose a new temporal reliable change detection (TRCD) algorithm to solve this drawback of fusion-based methods. Specifically, a potential and effective algorithm is proposed for learning temporal-reliable features for CD, which is achieved by designing a novel objective function. Unlike the traditional CD objective function, we impose a regular term in the objective function, which aims to enforce the extracted features before and after exchanging sequences of bi-temporal images that are similar to each other. In addition, our backbone architecture is designed based on a high-resolution network. The captured features are semantically richer and more spatially precise, which can improve the performance for small region changes. Comprehensive experimental results on two public datasets demonstrate that the proposed method is more advanced than other state-of-the-art (SOTA) methods, and our proposed objective function shows great potential. [ABSTRACT FROM AUTHOR]

The small defects in overhead catenary system (OCS) can result in long time delays, economic loss and even passenger injury. However, OCS images exhibit great variations with complex background and oblique views which pose a great challenge for small defects detection in high-speed rail system. In this paper, we propose the spatial-prior-guided attention for small object detection in OCS with two main advantages: (1) The spatial-prior is proposed to retain the spatial information between small defects and the electric components in OCS. (2) Based on spatial-prior, the spatial-prior-guided attention model (SAM) is designed to highlight useful information in the features and suppress redundant features response. SAM can model the spatial relations progressively and can be integrated with state-of-the-art feed-forward network architecture with end-to-end training fashion. We conduct extensive experiments on both Split pin datasets and PASCAL–VOC datasets and achieve 97.2% and 79.5% mAP values, respectively. All the experiments demonstrate the competitive performance of our method. [ABSTRACT FROM AUTHOR]

• We define a new inner interaction potential function based on the Skellam distribution. • The inner interaction potential function reflects the relationship of pixels in local neighborhood. • Our plug-and play scheme exploits both local and non-local priors of the image. • Our plug-and-play based algorithm can solve the new model efficiently. • Our method performs well for Poisson image denoising in photon-limited case. Photon-limited Poisson image denoising is urgent demand in many application fields, but is particularly challenging because the image structures are often damaged seriously. The effective using of the image prior is very important for improving the quality of the denoised image. In this paper, we exploit the prior of inner statistical relationships among pixels in local spatial neighborhood of the Poisson noisy image and introduce a new Skellam distribution based inner interaction potential function as a distance measurement between the pixels. In the framework of conditional random field (CRF) modeling, we propose a novel Poisson-Skellam distribution based regularization CRF model for photon-limited Poisson noise removal. Using the alternating direction method of multipliers technique, we extend our method to a flexible plug-and-play scheme in which we can combine powerful Gaussian denoising method to improve the denoising performance better. [ABSTRACT FROM AUTHOR]

Fine-grained visual classification (FGVC) is a difficult task due to the challenges of discriminative feature learning. Most existing methods directly use the final output of the network which always contains the global feature with high-level semantic information. However, the differences between fine-grained images are reflected in subtle local regions which often appear in the front of the network. When the texture of the background and object are similar or the proportion of the background is too large, the prediction will be greatly affected. In order to solve the above problems, this paper proposes multi-granularity feature fusion module (MGFF) and two-stage classification based on Vision-Transformer (ViT). The former comprehensively represents images by fusing features of different granularities, thus avoiding the limitations of single-scale features. The latter leverages the ViT model to separate the object from the background at a very small cost, thereby improving the accuracy of the prediction. We conduct comprehensive experiments and achieves the best performance in two fine-grained tasks on CUB-200-2011 and NA-Birds. • A multi-granularity feature fusion module is proposed to solve the limitations of single-scale features. • A two-stage classification based on Vision-Transformer is proposed to reduce background interference on predictions. By leveraging the ViT model, the object can be separated from the background and the details can be enlarged. • Extensive experiments prove the superiority of our model. The visualization results illustrate that our two-stage classification can accurately localize objects and facilitate correct predictions. [ABSTRACT FROM AUTHOR]

Recently, deep learning has drawn significant attention in hyperspectral image (HSI) classification. With the growth of network depth and feature integration, deep learning demands abundant labeled samples to optimize many parameters. Unfortunately, most hyperspectral data are unlabeled and the available labeled samples are extremely limited. How to obtain richer features under limited training samples is a challenge for HSI classification. To tackle this issue, a new supervised multiscale alternately updated clique network (MSCN) is proposed for HSI classification to fully employ HSI features in different scales. Based on the Clique Block, we design the multiscale alternately updated clique block (MSCB) that applies convolution kernels of various sizes to adaptively exploit the multiscale HSI information and merge them within the block. Meanwhile, the recurrent feedback architecture is introduced to reuse high-level visual information and network parameters. The proposed MSCN includes two MSCBs to capture the multiscale spectral and spatial information in turn. The MSCN improves the information flow and the efficiency of parameter tuning through the feedback mechanism and the cross-utilization of multiscale feature. It not only obtains more abstract HSI information, but also reduces the network depth and the number of parameters, thereby improving the classification accuracy under limited samples. To certify the validity of the proposed MSCN, experiments are conducted on three real HSI datasets and compared with multiple state-of-the-art deep learning-based approaches. The experimental results demonstrate that the presented multiscale network achieves superior performance, especially in the case of a small number of training samples. [ABSTRACT FROM AUTHOR]

Abstract: The paper “staircase effect alleviation by coupling gradient fidelity term” (Zhu Lixin and Xia Deshen, 2008 []) presented a nonlinear diffusion approach using a coupling gradient fidelity term. Although such approach helps to alleviate the staircase effect to some extent, the model is not sound. Moreover the physical mechanism of this model was explained incorrectly, and there are some theoretical mistakes in their proofs and propositions. We propose a new theoretical analysis of the alleviation staircase effect using a coupling gradient fidelity term. [Copyright &y& Elsevier]

Recently, the graph convolutional network (GCN) has drawn increasing attention in the hyperspectral image (HSI) classification. Compared with the convolutional neural network (CNN) with fixed square kernels, GCN can explicitly utilize the correlation between adjacent land covers and conduct flexible convolution on arbitrarily irregular image regions; hence, the HSI spatial contextual structure can be better modeled. However, to reduce the computational complexity and promote the semantic structure learning of land covers, GCN usually works on superpixel-based nodes rather than pixel-based nodes; thus, the pixel-level spectral–spatial features cannot be captured. To fully leverage the advantages of the CNN and GCN, we propose a heterogeneous deep network called CNN-enhanced GCN (CEGCN), in which CNN and GCN branches perform feature learning on small-scale regular regions and large-scale irregular regions, and generate complementary spectral–spatial features at pixel and superpixel levels, respectively. To alleviate the structural incompatibility of the data representation between the Euclidean data-oriented CNN and non-Euclidean data-oriented GCN, we propose the graph encoder and decoder to propagate features between image pixels and graph nodes, thus enabling the CNN and GCN to collaborate in a single network. In contrast to other GCN-based methods that encode HSI into a graph during preprocessing, we integrate the graph encoding process into the network and learn edge weights from training data, which can promote the node feature learning and make the graph more adaptive to HSI content. Extensive experiments on three data sets demonstrate that the proposed CEGCN is both qualitatively and quantitatively competitive compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Overhead catenary system (OCS) images exhibit great variations with clutter backgrounds, complex scenes and oblique views which pose great difficulty for automatic pillar number plate (NP) detection and recognition (NPDAR). Although these tasks have an important and practical significance for railway transportation, little researches have been done on these fields. In this paper, we propose a complete automatic NPDAR system with two main advantages: (1) For detection task, we propose Skip Connection Attention Module (SCAM) for adaptive feature refinement. Based on SCAM, the Attention_Guided Feature Fusion (AFF) module is designed for building high-level feature maps at different scales. (2) A novel convolution module, width/height convolution module (W/H-CM) was designed for NP recognition to capture global feature information efficiently. The W/H-CM extracts contextual information from two other perspectives compared to common convolution operation and iteratively generates supplementary information, making the representation of features more comprehensive. Both of them can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion. We conduct extensive experiments on both our datasets and standard benchmarks PASCAL-VOC, MS COCO to verify competitive performance of our method. [ABSTRACT FROM AUTHOR]

In this paper, we present a new construction of asymmetric quantum codes (AQCs) by combining classical concatenated codes (CCs) with tensor product codes (TPCs), called asymmetric quantum concatenated and tensor product codes (AQCTPCs) which have the following three advantages. First, only the outer codes in AQCTPCs need to satisfy the orthogonal constraint in quantum codes, and any classical linear code can be used for the inner, which makes AQCTPCs very easy to construct. Second, most AQCTPCs are highly degenerate, which means they can correct many more errors than their classical TPC counterparts. Consequently, we construct several families of AQCs with better parameters than known results in the literature. Third, AQCTPCs can be efficiently decoded although they are degenerate, provided that the inner and outer codes are efficiently decodable. In particular, we significantly reduce the inner decoding complexity of TPCs from Ω (n2an1)(a > 1) to O(n2) by considering error degeneracy, where n1 and n2 are the block length of the inner code and the outer code, respectively. Furthermore, we generalize our concatenation scheme by using the generalized CCs and TPCs correspondingly. [ABSTRACT FROM AUTHOR]

Deep Convolutional Neural Networks (CNN) have drawn great attention in image super-resolution (SR). Recently, visual attention mechanism, which exploits both of the feature importance and contextual cues, has been introduced to image SR and proves to be effective to improve CNN-based SR performance. In this paper, we make a thorough investigation on the attention mechanisms in a SR model and shed light on how simple and effective improvements on these ideas improve the state-of-the-arts. We further propose a unified approach called “multi-grained attention networks (MGAN)” which fully exploits the advantages of multi-scale and attention mechanisms in SR tasks. In our method, the importance of each neuron is computed according to its surrounding regions in a multi-grained fashion and then is used to adaptively re-scale the feature responses. More importantly, the “channel attention” and “spatial attention” strategies in previous methods can be essentially considered as two special cases of our method. We also introduce multi-scale dense connections to extract the image features at multiple scales and capture the features of different layers through dense skip connections. Ablation studies on benchmark datasets demonstrate the effectiveness of our method. In comparison with other state-of-the-art SR methods, our method shows the superiority in terms of both accuracy and model size. [ABSTRACT FROM AUTHOR]

Hyperspectral (HS) super-resolution, which aims at enhancing the spatial resolution of hyperspectral images (HSIs), has recently attracted considerable attention. A common way of HS super-resolution is to fuse the HSI with a higher spatial-resolution multispectral image (MSI). Various approaches have been proposed to solve this problem by establishing the degradation model of low spatial-resolution HSIs and MSIs based on matrix factorization methods, e.g., unmixing and sparse representation. However, this category of approaches cannot well construct the relationship between the high-spatial-resolution (HR) HSI and MSI. In fact, since the HSI and the MSI capture the same scene, these two image sources must have common factors. In this paper, a nonlocal tensor decomposition model for hyperspectral and multispectral image fusion (HSI-MSI fusion) is proposed. First, the nonlocal similar patch tensors of the HSI are constructed according to the MSI for the purpose of calculating the smooth order of all the patches for clustering. Then, the relationship between the HR HSI and the MSI is explored through coupled tensor canonical polyadic (CP) decomposition. The fundamental idea of the proposed model is that the factor matrices in the CP decomposition of the HR HSI’s nonlocal tensor can be shared with the matrices factorized by the MSI’s nonlocal tensor. Alternating direction method of multipliers is used to solve the proposed model. Through this method, the spatial structure of the MSI can be successfully transferred to the HSI. Experimental results on three synthetic data sets and one real data set suggest that the proposed method substantially outperforms the existing state-of-the-art HSI-MSI fusion methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • The H+-rich surface of the CoSx(t = 8)@Cu electrode builds an acid micro-environment. • Acid micro-environment realises self-regulation of EF systems from alkaline to acidic. • Amorphous CoSx(t = 8)@Cu/EF systems conquers the pH limitation of Fenton reaction and promotes Fe2+ regeneration. • Amorphous CoSx(t = 8)@Cu/EF systems present outstanding degradation performance and stability for organics pollutants. • CoSx(t = 8)@Cu/EF system can completely degrade SMX within 30 min while maintaining 80% Fe2+ recycling efficiency. Cathode materials with high hydrogen peroxide (H 2 O 2) selectivity and Fe2+ regeneration under macroneutral conditions are the essential for practical electro-Fenton processes. Herein, we develop an amorphous cobalt sulphide (CoSx(t = x)@Cu) cocatalytic electro-Fenton (EF) system capable of effectively degrading organic pollutants, such as sulfamethoxazole, at macroneutral pH conditions. Owing to the flexible electronic properties of amorphous structures and charge redistribution triggered by S defects, the optimum CoSx(t = 8)@Cu cathode successfully introduces H*/H+ and promotes the in-situ evolution of H 2 O 2. Additionally, an acidic microenvironment was constructed on the CoSx(t = 8)@Cu surface by the unreduced H*/H+, enabling a self-adjustment from alkalinity to acidity in the EF reaction without adding acid. Such self-adjustment effectually inhibits iron sludge production and promotes Fe2+ regeneration, assuring 80% Fe2+ cycling efficiency. More importantly, the EF system possesses outstanding stability and applicability for organic pollutant remediation regardless of water matrices. This work elucidates the intrinsic mechanism of amorphous electrocatalysts in overcoming the pH limitation of EF reactions, extending the applicability of Fenton technology. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) is often contaminated by mixed noise, which severely affects the visual quality and subsequent applications of the data. In this paper, HSI restoration based on low-rank recovery with a local neighborhood weighted spectral–spatial total variation (TV) model is proposed, which focuses on the preservation of spatial structure and spectral fidelity. The low-rank matrix model is adopted to exploit the spectral and spatial correlation information, and the $l_{1}$ -norm is used as a prior to remove the sparse noise. Furthermore, a local spatial neighborhood weighted spectral–spatial TV is utilized to jointly model the spectral–spatial prior information; specifically, the spectral and spatial differences are both considered in the TV term, and the weight is computed by considering the local neighborhood information in the spatial domain. Alternating direction method of multipliers optimization procedure is extended to solve the presented model. Experimental results demonstrate that the proposed method can remove the mixed noise, enhance the structural information simultaneously, and offer the best performance compared with several state-of-the-art HSI restoration methods. [ABSTRACT FROM AUTHOR]

Previous studies posited the effectiveness of stimulated recall. However, few studies explored how SR is implemented in a relatively static context, for example, online self‐directed learning, or took human factors, for example, cognitive style and gender, into consideration in such a context. To fill this gap, the current study, aims to introduce biofeedback as a stimulus for learners to engage in retrospection regarding their learning behavior. A quasi‐experimental design study was carried out over a 12‐week set of EFL self‐regulated online reading activities. Pretest and posttest on reading performance and their cognitive taxonomy were assessed through a developed scale instrument, whereas physiologicalsignals (e.g., gazing duration, verbal fixation, and brain wave) were captured via eye‐tracking and electroencephalograph (EEG) technology. The results emphasized that (a) students' reading ability and cognitive hierarchy significantly improved through biofeedback stimulation. Moreover, (b) learners in single level‐one cognitive hierarchic groups had significant improvements in both cognitive abilities and reading comprehension, whereas learners in multilevel hierarchic groups had no significant enhancements. Finally, (c) the optical data results and EEG reports showed that males favor procedural feedback and females have a preference for a conclusive assessment. Lay Description: What is already known about this topic: ·The stimulated recall (SR) technique, considered being a valuable tool for learners to capitalize on introspection, has positive effect on learning outcomes and cognitive processes in physical context.The recorded audio and video are generally used as the stimulus in physical learning context.The stimulus source may differ from a research context to another.Learning performance has close relationship with different human factors, such as cognitive taxonomy and gender differences. What this paper adds: Students adopting the biofeedback as stimulus demonstrated significant improvement in the dimensions of cognitive level and reading comprehension.Biofeedback, such as EEG and eye movement, may be applicable stimulus for stimulated recall in online self‐directed learning context.The lower cognitive students have more significant enhancement of taxonomy and reading capacity, when conducting biofeedback‐based stimulated recall.Eye‐track reports showed that males favor procedural feedback and females have a preference for the conclusive assessment. Implications for practice and/or policy: Biofeedback may act as a meta‐cognitive method to help learners realize their personalized learning habits and cognitive modes and encourage them to embark in the often daunting journey of autonomous learning.Biofeedback data could be used as valuable measurements for instructors to adjust their pedagogic design and improve teaching arrangements according to learners' emotional status and human factors.Procedural feedback is adaptive and should be considered utilizing for male students in self‐access learning context, whereas conclusive assessment approach is more applicable for female learners. [ABSTRACT FROM AUTHOR]

Study Objective: Nonsteroidal anti-inflammatory drugs (NSAIDs) play a role in pain relief, especially in postoperative pain caused by inflammation. They have demonstrated significant opioid dose-sparing effects, which help in reducing postoperative effects and opioid side effects. The objective of this meta-analysis was to explore the role of NSAIDs in reducing postoperative pain at different time intervals and provide reference for medication after lumbar spine surgery by a meta-analysis of randomized controlled trials (RCT).Design: A meta-analysis study of randomized controlled trials.Setting: Postoperative recovery area.Patients: Adult patients who have undergone lumbar spine surgery.Intervention: Patients received NSAIDs for pain control after lumbar spine surgery.Measurements: Standardized mean difference (SMD) and 95%CI were used to evaluate the visual analog scale of postoperative pain.Main Results: Four hundred and eight participants from eight studies were included in this study. The difference between the NSAIDs group and placebo is significant in 0-6, 12, and 24h groups (overall: SMD=-0.72, 95%CI -0.98 to -0.45; 0-6h: SMD=0.50, 95%CI -0.81 to -0.19; 12h: SMD=-1.07, 95%CI -1.45 to -0.70; 24h: SMD=-1.16, 95%CI -1.87 to -0.45). Heterogeneity and publication bias were observed in the 0-6 and 24h groups.Conclusion: NSAIDs are effective in postoperative analgesia after lumbar spine surgery. The study type, NSAID dose, different surgery types, and analgesic type might influence the efficacy of NSAIDs. [ABSTRACT FROM AUTHOR]

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l1-norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a 'connectivity strength-weighted sparse group constraint.' In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. Hum Brain Mapp 38:2370-2383, 2017. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Due to the increasing dimensionality and volume of remotely sensed hyperspectral data, the development of acceleration techniques for massive hyperspectral image analysis approaches is a very important challenge. Cloud computing offers many possibilities of distributed processing of hyperspectral datasets. This paper proposes a novel distributed parallel endmember extraction method based on iterative error analysis that utilizes cloud computing principles to efficiently process massive hyperspectral data. The proposed method takes advantage of technologies including MapReduce programming model, Hadoop Distributed File System (HDFS), and Apache Spark to realize distributed parallel implementation for hyperspectral endmember extraction, which significantly accelerates the computation of hyperspectral processing and provides high throughput access to large hyperspectral data. The experimental results, which are obtained by extracting endmembers of hyperspectral datasets on a cloud computing platform built on a cluster, demonstrate the effectiveness and computational efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

A novel method for anomaly detection in hyperspectral images (HSIs) is proposed based on low-rank and sparse representation. The proposed method is based on the separation of the background and the anomalies in the observed data. Since each pixel in the background can be approximately represented by a background dictionary and the representation coefficients of all pixels form a low-rank matrix, a low-rank representation is used to model the background part. To better characterize each pixel's local representation, a sparsity-inducing regularization term is added to the representation coefficients. Moreover, a dictionary construction strategy is adopted to make the dictionary more stable and discriminative. Then, the anomalies are determined by the response of the residual matrix. An important advantage of the proposed algorithm is that it combines the global and local structure in the HSI. Experimental results have been conducted using both simulated and real data sets. These experiments indicate that our algorithm achieves very promising anomaly detection performance. [ABSTRACT FROM PUBLISHER]

Colour image enhancement is an important preprocessing phase of many image analysis tasks such as image segmentation, pattern recognition and so on. This study presents a new local brightness adaptive variational model using Wasserstein distance for colour image enhancement. Under the perceptually inspired variational framework, the proposed energy functional consists of an improved contrast energy term and a Wasserstein dispersion energy term. To better adjust image dynamic range, the authors propose a local brightness adaptive contrast energy term using the average brightness of image local patch as the local brightness indicator. To restore image true colours, a Wasserstein distance‐based dispersion energy term is used to measure the statistical similarity between the original image and the enhanced image. The proposed energy functional is minimised by using a gradient descent algorithm. Two objective measures are used to quantitatively measure the enhancement quality. Experimental results demonstrate the efficiency of the proposed model for removing colour cast and haze, enhancing contrast, recovering details and equalising low key images. [ABSTRACT FROM AUTHOR]

Deep-learning methods rely on massive labeled data, which has become one of the main impediments in hyperspectral image change detection (HSI-CD). To resolve this problem, pseudo-labels generated by traditional methods are widely used to drive model learning. In this paper, we propose a mutual teaching approach with momentum correction for unsupervised HSI-CD to cope with noise in pseudo-labels, which is harmful for model training. First, we adopt two structurally identical models simultaneously, allowing them to select high-confidence samples for each other to suppress self-confidence bias, and continuously update pseudo-labels during iterations to fine-tune the models. Furthermore, a new group confidence-based sample filtering method is designed to obtain reliable training samples for HSI. This method considers both the quality and diversity of the selected samples by determining the confidence of each group instead of single instances. Finally, to better extract the spatial–temporal spectral features of bitemporal HSIs, a 3D convolutional neural network (3DCNN) is designed as an HSI-CD classifier and the basic network of our framework. Due to mutual teaching and dynamic label learning, pseudo-labels can be continuously updated and refined in iterations, and thus, the proposed method can achieve a better performance compared with those with fixed pseudo-labels. Experimental results on several HSI datasets demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]