Automatic Quaternion-Domain Color Image Stitching.

Taking advantages of the quaternion representation of the color image, this paper proposes a quaternion perceptual seamline detection model to generate the seamline in the quaternion domain. It considers seamline detection as a quaternion-domain color image labeling problem and minimizes the local-area quaternion perceptual difference cost to obtain the optimal seamline. To assess seamline quality effectively, we develop a quaternion perceptual seamline quality measure. Based on the proposed quaternion perceptual seamline detection model and quality measure, we further propose a general framework for automatic quaternion-domain color image stitching (AQCIS). To the best of our knowledge, this is the first attempt to perform color image stitching completely in the quaternion domain. Meanwhile, AQCIS introduces the joint optimization strategy of local alignment and seamline in an iterative fashion. Extensive experiments on challenging datasets demonstrate that our AQCIS achieves superior performance for color image stitching in comparison with state-of-the-art methods.

Recurrent Cerebral Infarction Due to Moyamoya Disease Complicated With Systemic Lupus Erythematosus: A Case Report and Literature Review.

INTRODUCTION: We report a rare case of moyamoya disease caused by an RNF213 mutation, complicated with systemic lupus erythematosus. CASE REPORT: A 32-year-old woman experienced 4 cerebral ischemia stroke events within 6 months. The main symptom was left limb weakness with blurred vision in the right eye. Results of digital subtraction angiography conducted at another hospital were consistent with moyamoya disease. On genetic testing, we found that the patient carried 2 mutations in the moyamoya disease-related gene RNF213 (p.R4810K, p.T1727M). On the basis of the laboratory immunologic indicators, such as positive antibodies and abnormal immunoglobulin levels and imaging examinations, the patient was finally diagnosed as moyamoya disease complicated with systemic lupus erythematosus. She was treated with aspirin, butylphthalide, urinary kallidinogenase, and sodium methylprednisolone. CONCLUSIONS: This was a 32-year-old young patient diagnosed with moyamoya disease carrying RNF213 gene mutation and accompanied by lupus with cerebral ischemic event as the first occurrence. The patient's condition was complex; therefore, comprehensive analysis and in-depth consideration were needed to avoid a missed diagnosis and misdiagnosis. When the primary disease cannot be identified, genetic testing can help to clarify the diagnosis of moyamoya disease.

Consistent Arbitrary Style Transfer Using Consistency Training and Self-Attention Module.

Arbitrary style transfer (AST) has garnered considerable attention for its ability to transfer styles infinitely. Although existing methods have achieved impressive results, they may overlook style consistencies and fail to capture crucial style patterns, leading to inconsistent style transfer (ST) caused by minor disturbances. To tackle this issue, we conduct a mathematical analysis of inconsistent ST and develop a style inconsistency measure (SIM) to quantify the inconsistencies between generated images. Moreover, we propose a consistent AST (CAST) framework that effectively captures and transfers essential style features into content images. The proposed CAST framework incorporates an intersection-of-union-preserving crop (IoUPC) module to obtain style pairs with minor disturbance, a self-attention (SA) module to learn the crucial style features, and a style inconsistency loss regularization (SILR) to facilitate consistent feature learning for consistent stylization. Our proposed framework not only provides an optimal solution for consistent ST but also outperforms existing methods when embedded into the CAST framework. Extensive experiments demonstrate that the proposed CAST framework can effectively transfer style patterns while preserving consistency and achieve the state-of-the-art performance.

Tensor Learning Meets Dynamic Anchor Learning: From Complete to Incomplete Multiview Clustering.

Multiview clustering (MVC), which can dexterously uncover the underlying intrinsic clustering structures of the data, has been particularly attractive in recent years. However, previous methods are designed for either complete or incomplete multiview only, without a unified framework that handles both tasks simultaneously. To address this issue, we propose a unified framework to efficiently tackle both tasks in approximately linear complexity, which integrates tensor learning to explore the inter-view low-rankness and dynamic anchor learning to explore the intra-view low-rankness for scalable clustering (TDASC). Specifically, TDASC efficiently learns smaller view-specific graphs by anchor learning, which not only explores the diversity embedded in multiview data, but also yields approximately linear complexity. Meanwhile, unlike most current approaches that only focus on pair-wise relationships, the proposed TDASC incorporates multiple graphs into an inter-view low-rank tensor, which elegantly models the high-order correlations across views and further guides the anchor learning. Extensive experiments on both complete and incomplete multiview datasets clearly demonstrate the effectiveness and efficiency of TDASC compared with several state-of-the-art techniques.

Gradient Learning With the Mode-Induced Loss: Consistency Analysis and Applications.

Variable selection methods aim to select the key covariates related to the response variable for learning problems with high-dimensional data. Typical methods of variable selection are formulated in terms of sparse mean regression with a parametric hypothesis class, such as linear functions or additive functions. Despite rapid progress, the existing methods depend heavily on the chosen parametric function class and are incapable of handling variable selection for problems where the data noise is heavy-tailed or skewed. To circumvent these drawbacks, we propose sparse gradient learning with the mode-induced loss (SGLML) for robust model-free (MF) variable selection. The theoretical analysis is established for SGLML on the upper bound of excess risk and the consistency of variable selection, which guarantees its ability for gradient estimation from the lens of gradient risk and informative variable identification under mild conditions. Experimental analysis on the simulated and real data demonstrates the competitive performance of our method over the previous gradient learning (GL) methods.

Detection of Deepfake Videos Using Long-Distance Attention.

With the rapid progress of deepfake techniques in recent years, facial video forgery can generate highly deceptive video content and bring severe security threats. And detection of such forgery videos is much more urgent and challenging. Most existing detection methods treat the problem as a vanilla binary classification problem. In this article, the problem is treated as a special fine-grained classification problem since the differences between fake and real faces are very subtle. It is observed that most existing face forgery methods left some common artifacts in the spatial domain and time domain, including generative defects in the spatial domain and interframe inconsistencies in the time domain. And a spatial-temporal model is proposed which has two components for capturing spatial and temporal forgery traces from a global perspective, respectively. The two components are designed using a novel long-distance attention mechanism. One component of the spatial domain is used to capture artifacts in a single frame, and the other component of the time domain is used to capture artifacts in consecutive frames. They generate attention maps in the form of patches. The attention method has a broader vision which contributes to better assembling global information and extracting local statistic information. Finally, the attention maps are used to guide the network to focus on pivotal parts of the face, just like other fine-grained classification methods. The experimental results on different public datasets demonstrate that the proposed method achieves state-of-the-art performance, and the proposed long-distance attention method can effectively capture pivotal parts for face forgery.

The effect of regulating MCU expression on experimental ischemic brain injury.

Mitochondrial calcium uniporter (MCU) is a critical channel for Ca2+ influx into mitochondria. The present study aimed to determine if MCU knockdown has beneficial effects on ischemic brain injury and to explore the underlying mechanisms. The present study demonstrated that MCU knockdown but not total knockout (KO) attenuated ischemia infarction volume and primary cortical neuronal cells' ischemic damage. MCU knockdown maintained mitochondrial ultrastructure, alleviated calcium overload, and reduced mitochondrial apoptosis. Moreover, MCU knockdown regulated the changes of MICU1 and MICU2 after cerebral infarction, while no changes were observed in other mitochondrial calcium handling proteins. Based on metabolomics, MCU knockdown reversed middle cerebral artery occlusion (MCAO)-induced up-regulated phosphoenolpyruvate and down-regulated GDP to protect energy metabolism after cerebral infarction. Furthermore, a total of 87 and 245 differentially expressed genes (DEGs) were detected by transcriptome sequencing among WT mice, MCU KO mice and MCU knockdown mice in the MCAO model, respectively. Then, NR4A1 was identified as one of the DEGs in different MCU expressions in vivo ischemia stroke model via transcriptomic screening and genetic validation. Furthermore, MCU knockdown downregulated the ischemia-induced upregulation of NR4A1 expression. Together, this is the further evidence that the MCU knockdown exerts a protective role after cerebral infarction by promoting calcium homeostasis, inhibiting mitochondrial apoptosis and protecting energy metabolism.

LMFFNet: A Well-Balanced Lightweight Network for Fast and Accurate Semantic Segmentation.

Real-time semantic segmentation is widely used in autonomous driving and robotics. Most previous networks achieved great accuracy based on a complicated model involving mass computing. The existing lightweight networks generally reduce the parameter sizes by sacrificing the segmentation accuracy. It is critical to balance the parameters and accuracy for real-time semantic segmentation. In this article, we propose a lightweight multiscale-feature-fusion network (LMFFNet) mainly composed of three types of components: split-extract-merge bottleneck (SEM-B) block, feature fusion module (FFM), and multiscale attention decoder (MAD), where the SEM-B block extracts sufficient features with fewer parameters. FFMs fuse multiscale semantic features to effectively improve the segmentation accuracy and the MAD well recovers the details of the input images through the attention mechanism. Without pretraining, LMFFNet-3-8 achieves 75.1% mean intersection over union (mIoU) with 1.4 M parameters at 118.9 frames/s using RTX 3090 GPU. More experiments are investigated extensively on various resolutions on other three datasets of CamVid, KITTI, and WildDash2. The experiments verify that the proposed LMFFNet model makes a decent tradeoff between segmentation accuracy and inference speed for real-time tasks. The source code is publicly available at https://github.com/Greak-1124/LMFFNet.

ExS-GAN: Synthesizing Anti-Forensics Images via Extra Supervised GAN.

So far, researchers have proposed many forensics tools to protect the authenticity and integrity of digital information. However, with the explosive development of machine learning, existing forensics tools may compromise against new attacks anytime. Hence, it is always necessary to investigate anti-forensics to expose the vulnerabilities of forensics tools. It is beneficial for forensics researchers to develop new tools as countermeasures. To date, one of the potential threats is the generative adversarial networks (GANs), which could be employed for fabricating or forging falsified data to attack forensics detectors. In this article, we investigate the anti-forensics performance of GANs by proposing a novel model, the ExS-GAN, which features an extra supervision system. After training, the proposed model could launch anti-forensics attacks on various manipulated images. Evaluated by experiments, the proposed method could achieve high anti-forensics performance while preserving satisfying image quality. We also justify the proposed extra supervision via an ablation study.

CVIDS: A Collaborative Localization and Dense Mapping Framework for Multi-Agent Based Visual-Inertial SLAM.

Nowadays, visual SLAM (Simultaneous Localization And Mapping) has become a hot research topic due to its low costs and wide application scopes. Traditional visual SLAM frameworks are usually designed for single-agent systems, completing both the localization and the mapping with sensors equipped on a single robot or a mobile device. However, the mobility and work capacity of the single agent are usually limited. In reality, robots or mobile devices sometimes may be deployed in the form of clusters, such as drone formations, wearable motion capture systems, and so on. As far as we know, existing SLAM systems designed for multi-agents are still sporadic, and most of them have non-negligible limitations in functions. Specifically, on one hand, most of the existing multi-agent SLAM systems can only extract some key features and build sparse maps. On the other hand, schemes that can reconstruct the environment densely cannot get rid of the dependence on depth sensors, such as RGBD cameras or LiDARs. Systems that can yield high-density maps just with monocular camera suites are temporarily lacking. As an attempt to fill in the research gap to some extent, we design a novel collaborative SLAM system, namely CVIDS (Collaborative Visual-Inertial Dense SLAM), which follows a centralized and loosely coupled framework and can be integrated with any existing Visual-Inertial Odometry (VIO) to accomplish the co-localization and the dense reconstruction. Integrating our proposed robust loop closure detection module and two-stage pose-graph optimization pipeline, the co-localization module of CVIDS can estimate the poses of different agents in a unified coordinate system efficiently from the packed images and local poses sent by the client-ends of different agents. Besides, our motion-based dense mapping module can effectively recover the 3D structures of selected keyframes and then fuse their depth information to the global map for reconstruction. The superior performance of CVIDS is corroborated by both quantitative and qualitative experimental results. To make our results reproducible, the source code has been released at https://cslinzhang.github.io/CVIDS.

Efficient Harmonic Neural Networks With Compound Discrete Cosine Transform Filters and Shared Reconstruction Filters.

The harmonic neural network (HNN) learns a combination of discrete cosine transform (DCT) filters to obtain an integrated feature from all spectra in the frequency domain. HNN, however, faces two challenges in learning and inference processes. First, the spectrum feature learned by HNN is insufficient and limited because the number of DCT filters is much smaller than that of feature maps. In addition, the number of parameters and the computation costs of HNN are significantly high because the intermediate spectrum layers are expanded multiple times. These two challenges will severely harm the performance and efficiency of HNN. To solve these problems, we first propose the compound DCT (C-DCT) filters integrating the nearest DCT filters to retrieve rich spectrum features to improve the performance. To significantly reduce the model size and computation complexity for improving the efficiency, the shared reconstruction filter is then proposed to share and dynamically drop the meta-filters in every frequency branch. Integrating the C-DCT filters with the shared reconstruction filters, the efficient harmonic network (EH-Net) is introduced. Extensive experiments on different datasets demonstrate that the proposed EH-Nets can effectively reduce the model size and computation complexity while maintaining the model performance. The code has been released at https://github.com/zhangle408/EH-Nets.

Viscous Solvent-Assisted Planetary Ball Milling for the Scalable Production of Large Ultrathin Two-Dimensional Materials.

Ball milling is a widely used method to produce graphene and other two-dimensional (2D) materials for both industry and research. Conventional ball milling generates strong impact forces, producing small and thick nanosheets that limit their applications. In this study, a viscous solvent-assisted planetary ball milling method has been developed to produce large thin 2D nanosheets. The viscous solvent simultaneously increases the exfoliation energy (Ee) and lowers the impact energy (Ei). Simulations show a giant ratio of η = Ee/Ei, for the viscous solvent, 2 orders of magnitude larger than that of water. The method provides both a high exfoliation yield of 74%, a high aspect ratio of the generated nanosheets of 571, and a high quality for a representative 2D material of boron nitride nanosheets (BNNSs). The large thin BNNSs can be assembled into high-performance functional films, such as separation membranes and thermally conductive flexible films with some performance parameters better than those 2D nanosheets produced by chemical exfoliation methods.

Impulse Noise Image Restoration Using Nonconvex Variational Model and Difference of Convex Functions Algorithm.

In this article, the problem of impulse noise image restoration is investigated. A typical way to eliminate impulse noise is to use an L1 norm data fitting term and a total variation (TV) regularization. However, a convex optimization method designed in this way always yields staircase artifacts. In addition, the L1 norm fitting term tends to penalize corrupted and noise-free data equally, and is not robust to impulse noise. In order to seek a solution of high recovery quality, we propose a new variational model that integrates the nonconvex data fitting term and the nonconvex TV regularization. The usage of the nonconvex TV regularizer helps to eliminate the staircase artifacts. Moreover, the nonconvex fidelity term can detect impulse noise effectively in the way that it is enforced when the observed data is slightly corrupted, while is less enforced for the severely corrupted pixels. A novel difference of convex functions algorithm is also developed to solve the variational model. Using the variational method, we prove that the sequence generated by the proposed algorithm converges to a stationary point of the nonconvex objective function. Experimental results show that our proposed algorithm is efficient and compares favorably with state-of-the-art methods.

Secure Halftone Image Steganography Based on Feature Space and Layer Embedding.

Syndrome-trellis codes (STCs) are commonly used in image steganographic schemes, which aim at minimizing the embedding distortion, but most distortion models cannot capture the mutual interaction of embedding modifications (MIEMs). In this article, a secure halftone image steganographic scheme based on a feature space and layer embedding is proposed. First, a feature space is constructed by a characterization method that is designed based on the statistics of 4 ×4 pixel blocks in halftone images. Upon the feature space, a generalized steganalyzer with good classification ability is proposed, which is used to measure the embedding distortion. As a result, a distortion model based on a hybrid feature space is constructed, which outperforms some state-of-the-art models. Then, as the distortion model is established on the statistics of local regions, a layer embedding strategy is proposed to reduce MIEM. It divides the host image into multiple layers according to their relative positions in 4 ×4 blocks, and the embedding procedure is executed layer by layer. In each layer, any two pixels are located at different 4 ×4 blocks in the original image, and the distortion model makes sure that the calculation of pixel distortions is independent. Between layers, the pixel distortions of the current layer are updated according to the previous embedding modifications, thus reducing the total embedding distortion. Comparisons with prior schemes demonstrate that the proposed steganographic scheme achieves high statistical security when resisting the state-of-the-art steganalysis.

PID Controller-Guided Attention Neural Network Learning for Fast and Effective Real Photographs Denoising.

Real photograph denoising is extremely challenging in low-level computer vision since the noise is sophisticated and cannot be fully modeled by explicit distributions. Although deep-learning techniques have been actively explored for this issue and achieved convincing results, most of the networks may cause vanishing or exploding gradients, and usually entail more time and memory to obtain a remarkable performance. This article overcomes these challenges and presents a novel network, namely, PID controller guide attention neural network (PAN-Net), taking advantage of both the proportional-integral-derivative (PID) controller and attention neural network for real photograph denoising. First, a PID-attention network (PID-AN) is built to learn and exploit discriminative image features. Meanwhile, we devise a dynamic learning scheme by linking the neural network and control action, which significantly improves the robustness and adaptability of PID-AN. Second, we explore both the residual structure and share-source skip connections to stack the PID-ANs. Such a framework provides a flexible way to feature residual learning, enabling us to facilitate the network training and boost the denoising performance. Extensive experiments show that our PAN-Net achieves superior denoising results against the state-of-the-art in terms of image quality and efficiency.

Addi-Reg: A Better Generalization-Optimization Tradeoff Regularization Method for Convolutional Neural Networks.

In convolutional neural networks (CNNs), generating noise for the intermediate feature is a hot research topic in improving generalization. The existing methods usually regularize the CNNs by producing multiplicative noise (regularization weights), called multiplicative regularization (Multi-Reg). However, Multi-Reg methods usually focus on improving generalization but fail to jointly consider optimization, leading to unstable learning with slow convergence. Moreover, Multi-Reg methods are not flexible enough since the regularization weights are generated from a definite manual-design distribution. Besides, most popular methods are not universal enough, because these methods are only designed for the residual networks. In this article, we, for the first time, experimentally and theoretically explore the nature of generating noise in the intermediate features for popular CNNs. We demonstrate that injecting noise in the feature space can be transformed to generating noise in the input space, and these methods regularize the networks in a Mini-batch in Mini-batch (MiM) sampling manner. Based on these observations, this article further discovers that generating multiplicative noise can easily degenerate the optimization due to its high dependence on the intermediate feature. Based on these studies, we propose a novel additional regularization (Addi-Reg) method, which can adaptively produce additional noise with low dependence on intermediate feature in CNNs by employing a series of mechanisms. Particularly, these well-designed mechanisms can stabilize the learning process in training, and our Addi-Reg method can pertinently learn the noise distributions for every layer in CNNs. Extensive experiments demonstrate that the proposed Addi-Reg method is more flexible and universal, and meanwhile achieves better generalization performance with faster convergence against the state-of-the-art Multi-Reg methods.

Adaptive Transition Probability Matrix Learning for Multiview Spectral Clustering.

Multiview clustering as an important unsupervised method has been gathering a great deal of attention. However, most multiview clustering methods exploit the self-representation property to capture the relationship among data, resulting in high computation cost in calculating the self-representation coefficients. In addition, they usually employ different regularizers to learn the representation tensor or matrix from which a transition probability matrix is constructed in a separate step, such as the one proposed by Wu et al.. Thus, an optimal transition probability matrix cannot be guaranteed. To solve these issues, we propose a unified model for multiview spectral clustering by directly learning an adaptive transition probability matrix (MCA2M), rather than an individual representation matrix of each view. Different from the one proposed by Wu et al., MCA2M utilizes the one-step strategy to directly learn the transition probability matrix under the robust principal component analysis framework. Unlike existing methods using the absolute symmetrization operation to guarantee the nonnegativity and symmetry of the affinity matrix, the transition probability matrix learned from MCA2M is nonnegative and symmetric without any postprocessing. An alternating optimization algorithm is designed based on the efficient alternating direction method of multipliers. Extensive experiments on several real-world databases demonstrate that the proposed method outperforms the state-of-the-art methods.

Robust Estimation of Upscaling Factor on Double JPEG Compressed Images.

As one of the most important topics in image forensics, resampling detection has developed rapidly in recent years. However, the robustness to JPEG compression is still challenging for most classical spectrum-based methods, since JPEG compression severely degrades the image contents and introduces block artifacts in the boundary of the compression grid. In this article, we propose a method to estimate the upscaling factors on double JPEG compressed images in the presence of image upscaling between the two compressions. We first analyze the spectrum of scaled images and give an overall formulation of how the scaling factors along with the parameters of JPEG compression and image contents influence the appearance of tampering artifacts. The expected positions of five kinds of characteristic peaks are analytically derived. Then, we analyze the features of double JPEG compressed images in the block discrete cosine transform (BDCT) domain and present an inverse scaling strategy for the upscaling factor estimation with a detailed proof. Finally, a fusion method is proposed that through frequency-domain analysis, a candidate set of upscaling factors is given, and through analysis in the BDCT domain, the optimal estimation from all candidates is determined. The experimental results demonstrate that the proposed method outperforms other state-of-the-art methods.

Simulation of Atmospheric Visibility Impairment.

Changes in aerosol composition and its proportions can cause changes in atmospheric visibility. Vision systems deployed outdoors must take into account the negative effects brought by visibility impairment. In order to develop vision algorithms that can adapt to low atmospheric visibility conditions, a large-scale dataset containing pairs of clear images and their visibility-impaired versions (along with other annotations if necessary) is usually indispensable. However, it is almost impossible to collect large amounts of such image pairs in a real physical environment. A natural and reasonable solution is to use virtual simulation technologies, which is also the focus of this paper. In this paper, we first deeply analyze the limitations and irrationalities of the existing work specializing on simulation of atmospheric visibility impairment. We point out that many simulation schemes actually even violate the assumptions of the Koschmieder's law. Second, more importantly, based on a thorough investigation of the relevant studies in the field of atmospheric science, we present simulation strategies for five most commonly encountered visibility impairment phenomena, including mist, fog, natural haze, smog, and Asian dust. Our work establishes a direct link between the fields of atmospheric science and computer vision. In addition, as a byproduct, with the proposed simulation schemes, a large-scale synthetic dataset is established, comprising 40,000 clear source images and their 800,000 visibility-impaired versions. To make our work reproducible, source codes and the dataset have been released at https://cslinzhang.github.io/AVID/.

Generalized Nonconvex Low-Rank Tensor Approximation for Multi-View Subspace Clustering.

The low-rank tensor representation (LRTR) has become an emerging research direction to boost the multi-view clustering performance. This is because LRTR utilizes not only the pairwise relation between data points, but also the view relation of multiple views. However, there is one significant challenge: LRTR uses the tensor nuclear norm as the convex approximation but provides a biased estimation of the tensor rank function. To address this limitation, we propose the generalized nonconvex low-rank tensor approximation (GNLTA) for multi-view subspace clustering. Instead of the pairwise correlation, GNLTA adopts the low-rank tensor approximation to capture the high-order correlation among multiple views and proposes the generalized nonconvex low-rank tensor norm to well consider the physical meanings of different singular values. We develop a unified solver to solve the GNLTA model and prove that under mild conditions, any accumulation point is a stationary point of GNLTA. Extensive experiments on seven commonly used benchmark databases have demonstrated that the proposed GNLTA achieves better clustering performance over state-of-the-art methods.