Search

As we know, feedforward neural networks (FNNs) with sigmoidal activation function are universal approximators. Theoretically, for any continuous function defined on a compact set, there exists an FNN such that the FNN can approximate the function with arbitrary accuracy, which constitutes a theoretical guarantee that FNN can be used as an efficient learning machine. This paper addresses the construction and approximation for FNNs. We construct an FNN with sigmoidal activation function and estimate its approximation error. In particular, an inverse theorem of the approximation is established, which implies the equivalence characterization theorem of the approximation and reveals the relationship between the topological structure of the FNN and its approximation ability. As keys in this study, the concepts of modulus of continuity of function, K-functional, and their relationship are utilized, and two Bernstein-type inequalities are established. [ABSTRACT FROM AUTHOR]

Existing deep learning-based image super-resolution (SR) algorithms have made great achievements on synthetic LR–HR pairs in a fully supervised manner. However, the real image degradation process is complicated and unclear, leading to a domain gap between the synthetic LR images and real LR images. To address this issue, the most widely used alternatives are GAN-based methods trained with unpaired data. But most GAN-based methods tend to follow complex multi-stage pipelines and still suffer from the vast cost of hyperparameter tuning on the loss function. In this paper, in contrast to complicated two-stage or multi-stage GAN, a straightforward and simple one-stage GAN-based framework is proposed. Different from the current one-stage GAN, our approach exclusively fits the CycleGAN pattern without any other restrictions, fully leveraging images from unpaired datasets. Specifically, our framework consists of a DegradeNet and an SRNet, which implicitly models the degradation and SR process between the source LR domain and target HR domain. Such a simple one-stage GAN allows us to flexibly learn the data distribution without any assumptions, greatly reducing the model complexity and training difficulty. To further address the inherent practice of empirically setting hyperparameters in previous methods, we propose an adaptive weighting network to automatically learn the contribution weights of different losses in a meta-learning manner. This flexible strategy could shorten the gap between various losses and meanwhile help the model to adaptively optimize in an efficient way. Extensive experiments demonstrate the superiority of our proposed method in terms of indicators and perceptual quality. [ABSTRACT FROM AUTHOR]

The main purpose of negative correlation learning (NCL) is to produce ensembles with sound generalization capability through controlling the disagreement among base learners’ outputs. This paper uses neural networks with random weights (NNRWs) to implement such learning scheme in the study of face recognition. Particularly, two-dimensional (2D) feed-forward neural networks (2D-FNNs) with random weights (2D-NNRWs) are employed as base components, and which are incorporated with the NCL strategy for building neural network ensembles, where the basis functions of the base networks are generated randomly and the free parameters of the 2D-FNNs can be determined by solving a linear equation system. Also, an analytical solution is derived for these parameters. To examine the merits of the proposed algorithm, a series of comparative experiments are performed. The experimental results indicate that the proposed approach outperforms existing approaches. [ABSTRACT FROM AUTHOR]

Robust modelling is significant to deal with complex systems with uncertainties. This paper aims to develop a novel learning algorithm for training regularized local random weights networks (RWNs). The learner model, terms as RL-RWN, is built on regularized moving least squares method and generalizes the solution obtained from the standard least square technique. Simulations are carried out using two benchmark datasets, including Auto-MPG data and surface reconstruction data. Results demonstrate that our proposed RL-RWN outperforms the original RWN and radial basis function networks. [ABSTRACT FROM AUTHOR]

The synchronization problem of linearly coupled singular systems is investigated. The uncoupled dynamical behavior at each node is general and can be chaotic or otherwise; the coupling configuration is also general, with the coupling matrix not assumed to be symmetric or irreducible. The existence and uniqueness of the solution of coupled system and the existence and uniqueness of the synchronization state are explored. Some sufficient conditions for globally exponential synchronization are derived based on Lyapunov stability theory. These criteria, which are in terms of linear matrix inequality (LMI), indicate that the left and right eigenvector corresponding to eigenvalue zero of the coupling matrix play key roles in the stability analysis of the synchronization manifold. Finally, a numerical example is provided to illustrate the effectiveness of the proposed conditions. [ABSTRACT FROM AUTHOR]

Although deep neural networks have shown good performance on grid data, it is challenging to design deep neural networks for point cloud processing due to the irregular domain and disordering of point cloud. This paper presents a novel convolution block named edge-point features mixed convolution (EPFM-Conv) which effectively integrates the graph-based method and the point-based strategy. The entire EPFM-Conv block constructs the edge branch and point branch to extract the local and global features of point cloud, respectively. In the edge branch, we first construct the local dynamic graphs on point cloud and thereby extract rich detailed features through edge convolution. Next, the features of the edge are further abstracted and aggregated through the grouped residual learning followed by a symmetric function. In the point branch, we directly extract the features of the point by a point-wise multi-layer perceptron. Finally, the features of edge and point are mixed adaptively to obtain the feature representation of point cloud. Compared with the existing methods, the main difference is that the proposed method dexterously constructs the local dynamic graphs on point cloud for extracting rich detailed features, and synchronously designs the edge and point branches to extract the local and global features. Meanwhile, the proposed method uses the grouped residual learning to improve the information flow, thereby effectively deepening the depth of the network. Extensive experiments demonstrate that the proposed EPFM-Conv achieves the excellent performances on shape classification as well as part segmentation tasks. [ABSTRACT FROM AUTHOR]

Class imbalance is a challenge in image segmentation. Deep learning has demonstrated potential advantages as a promising approach in solving such challenging problems because of its powerful ability to extract features. To treat class imbalance in medical image segmentation effectively, in this study, deep learning technology is employed. Further, a new approach, referred to as multiscale fused network with additive channel–spatial attention (MSF-ACSA), is developed. In particular, using the U-shape convolutional network as the structure of the main body network, an additive channel–spatial attention (ACSA) module, which exploits high-level features to guide low-level features for optimal responses, is designed. Therefore, the proposed MSF-ACSA can availably focus on regions of interest while adaptively recalibrating the channel significance of feature maps. Simultaneously, a multiscale fusion with deep supervision module is introduced to incorporate fine-grained details with coarse-grained semantics from diverse scale features. Furthermore, a focal weighted Tversky loss function is proposed to mitigate class imbalance issues. Numerous experimental results indicate that the proposed MSF-ACSA achieves the best performance when compared with state-of-the-art approaches for medical image segmentation. [ABSTRACT FROM AUTHOR]

In general, the deeper the convolutional network is, the higher the accuracy will be. However, when the depth of the network increases, the number of parameters (weights and bias) will increase quickly, which requires a lot of storage space when it is applied in real life. Considering the parameters' efficiency for the deep network, we propose an improved deep convolutional network, named Recursive Dense Convolutional Network (RDenseNet), inspired by the recursive idea. The proposed RDenseNet still utilizes the dense block in Dense Convolutional Network (DenseNet) as a basic structure, but different from the cascade structure of DenseNet, our proposed RDenseNet uses the output of the dense block as input for the current block again. That is, our proposed RDenseNet turns the cascade structure in DenseNet into a recursive structure, which can reduce the number of parameters that actually need to be stored. Experiments illustrate that our proposed RDenseNet can still keep or exceed the accuracy of some state-of-the-art deep networks with much less parameters. [ABSTRACT FROM AUTHOR]

Several learning-based methods dedicated to 3D point cloud registration have been developed recently. However, the performance of such methods is limited due to a lack of effective feature interaction mechanisms. To overcome this problem, an iteration-based interactive attention network is proposed here for 3D point cloud registration that can effectively learn the overlapping features of point cloud pairs and improve the registration accuracy. Concretely, we first designed a condensed global information extractor to achieve aggregated global features. Meanwhile, to reduce the amount of calculation required for subsequent modules while maintaining high-level characteristic information, the number of point cloud features is reduced. Furthermore, an overlapping feature iterative attention module and a discriminant feature iterative expansion module are designed. The main function of both modules is to implement feature interaction in the form of cross-attention and self-attention as well as to deepen interactions iteratively. The former captures overlapping information by iteratively interacting with the features of the condensed global features of one point cloud and the local geometric features of another point cloud. The latter can expand the number of features back to the initial number of point clouds through the interaction of overlapping features and local geometric features. Finally, matching features with high discriminative ability are obtained and used to formulate a soft correspondence matrix. Furthermore, weighted singular-value decomposition is adopted to obtain the rigid transformation matrix. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Highlights • Propose a new super-resolution method based on CSC and CNN. • Use CNN to reconstruct the high resolution image for the low-frequency part. • Propose an ACSC to reconstruct high resolution image for high-frequency part. • Integrate and develope the advantages of the CSC method and CNN. Abstract The convolutional sparse coding-based super-resolution (CSC-SR) method has shown its good performance in single image super-resolution. It divides the low-resolution (LR) image into low-frequency part and the high-frequency part, and reconstructs their corresponding high-resolution (HR) image with bicubic interpolation and convolutional sparse coding (CSC) method, respectively. This paper is devoted to improve the performance of CSC-SR method. As convolutional neural network (CNN) can reveal the mapping relation between the LR image and the HR image for the low-frequency part better, we replace the bicubic interpolation with CNN to reconstruct the HR image for the low-frequency part. In addition, we propose an adaptive CSC method to reconstruct the HR image for the high-frequency part. We name our proposed super-resolution method as hybrid adaptive convolutional sparse coding-based super-resolution (HACSC-SR) method. Many comparison experiments illustrate that our proposed HACSC-SR method is superior to CSC-SR, CNN as well as several existing super-resolution methods. [ABSTRACT FROM AUTHOR]

Since neural networks possess universal approximation property and the property can be a theoretical guarantee for image inpainting, the research, which uses deep learning technology to complete images, has attracted more attention from researchers in recent years. Given that structure and texture are two factors in the image, some existing studies have shown that modeling structure and texture can improve the effect of image inpainting. However, there exist close relationships between structure and texture, and they can contribute to each other, which is not considered in the existing works. This paper not only models the structure and texture of the image, but also constructs a bidirectional interactive relationship between them. A two-stream coupling network for image inpainting, which can effectively perform the bidirectional interaction between structure and texture, is proposed. Specifically, two parallel encoder–decoder networks are constructed to be responsible for structure reconstruction and texture synthesis respectively. Meanwhile, two interaction mechanisms called gated interaction unit (GIU) and spatial-channel attention (SCIM), are embedded between them, which perform bidirectional interaction to structure and texture information. GIU weighs the interaction between structure and texture features through gating mechanisms, and SCIM achieves a deeper interaction to the distant contexts of structure and texture through spatial-channel attention mechanisms. In this way, the network promotes both structure-guided texture synthesis and texture-facilitated structure reconstruction. Extensive experiments illustrate the superiority of the proposed method. • A two-stream coupling network with bidirectional interaction between structure and texture complete is designed. • A gated interaction unit (GIU) and a spatial-channel interaction module (SCIM) are proposed. • Frequent mutual perception between structure and texture is established. • Structure and texture feature pyramids are constructed respectively, ensuring that the structure and texture of the image can be gradually restored. [ABSTRACT FROM AUTHOR]

There have been a lot of methods to address the recognition of complete face images. However, in real applications, the images to be recognized are usually incomplete, and it is more difficult to realize such a recognition. In this paper, a novel convolution neural network frame, named a low-rank-recovery network (LRRNet), is proposed to conquer the difficulty effectively inspired by matrix completion and deep learning techniques. The proposed LRRNet first recovers the incomplete face images via an approach of matrix completion with the truncated nuclear norm regularization solution, and then extracts some low-rank parts of the recovered images as the filters. With these filters, some important features are obtained by means of the binaryzation and histogram algorithms. Finally, these features are classified with the classical support vector machines (SVMs). The proposed LRRNet method has high face recognition rate for the heavily corrupted images, especially for the images in the large databases. The proposed LRRNet performs well and efficiently for the images with heavily corrupted, especially in the case of large databases. Extensive experiments on several benchmark databases demonstrate that the proposed LRRNet performs better than some other excellent robust face recognition methods. [ABSTRACT FROM AUTHOR]

The detection and classification of white blood cells (WBCs, also known as Leukocytes) is a hot issue because of its important applications in disease diagnosis. Nowadays the morphological analysis of blood cells is operated manually by skilled operators, which results in some drawbacks such as slowness of the analysis, a non-standard accuracy, and the dependence on the operator's skills. Although there have been many papers studying the detection of WBCs or classification of WBCs independently, few papers consider them together. This paper proposes an automatic detection and classification system for WBCs from peripheral blood images. It firstly proposes an algorithm to detect WBCs from the microscope images based on the simple relation of colors R, B and morphological operation. Then a granularity feature (pairwise rotation invariant co-occurrence local binary pattern, PRICoLBP feature) and SVM are applied to classify eosinophil and basophil from other WBCs firstly. Lastly, convolution neural networks are used to extract features in high level from WBCs automatically, and a random forest is applied to these features to recognize the other three kinds of WBCs: neutrophil, monocyte and lymphocyte. Some detection experiments on Cellavison database and ALL-IDB database show that our proposed detection method has better effect almost than iterative threshold method with less cost time, and some classification experiments show that our proposed classification method has better accuracy almost than some other methods. [ABSTRACT FROM AUTHOR]

Point cloud data obtained by 3D scanning equipment is often incomplete. In recent years, to complete the missing point clouds has become an important task in computer visualization research. This paper adopts a coarse-to-fine completion strategy and build a novel adaptive region shape fused network (ARSF-Net), which contains three core modules, namely region shape encoding module (RSE), adaptive feature selection-aggregation module (ASA), and encoding-attention transformer module (EAT). The RSE module adaptively aggregates the latent shape information contained in local features according to the feature strength. In ASA module, we first treat point coordinates and shape features as parent nodes and design a hybrid correlation method to adaptively group parent nodes. Then, each set of parent nodes generates a child node. Finally, we splice the features and points in the parent node and child node separately to double the number. For EAT module, we learn features from the encoding stage and use a coordinate-based embedding transformer to generate uniform high-resolution point clouds. Compared with previous methods, we pay special attention to the difference among the latent shape information contained in the local point clouds, thus making the local feature extraction more interpretable. At the same time, to generate valid detail features from the original ones, we abundantly consider the correlation among the original ones, and directly combine the original features with the generated ones. Our experiments on different datasets verify the good performance of ARSF-Net in the point cloud completion task. [ABSTRACT FROM AUTHOR]

This paper designs a novel human face recognition method, which is mainly based on a new feature extraction method and an efficient classifier – random weight network (RWN). Its innovation of the feature extraction is embodied in the good fusion of the geometric features and algebraic features of the original image. Here the geometric features are acquired by means of fast discrete curvelet transform (FDCT) and 2-dimensional principal component analysis (2DPCA), while the algebraic features are extracted by a proposed quasi-singular value decomposition (Q-SVD) method that can embody the relations of each image under a unified framework. Subsequently, the efficient RWN is applied to classify these fused features to further improve the recognition rate and the recognition speed. Some comparison experiments are carried out on six famous face databases between our proposed method and some other state-of-the-art methods. The experimental results show that the proposed method has an outstanding superiority in the aspects of separability, recognition rate and training time. [ABSTRACT FROM AUTHOR]

3D point clouds classification is fundamental but always challenging in point clouds analysis, of which the key is efficiently extracting their distinguishing features. At present, several studies based on deep learning perform well in 3D point clouds classification task but are still weak in the capability of resemblance categories recognition. This paper presents a component semantic recognizer to decrease misclassification between the resemblance categories, which can better identify the different structural blocks of point clouds. Specifically, a point energy measurement unit is first built to pre-divide point clouds into non-overlapping blocks. Then, considering the fact that the closer the distance between 3D points, the stronger the feature interaction should be, an affinity bias is proposed for preserving the spatial properties when points features interact within the structural block. Besides validating the method on both synthetic and real-world point clouds benchmarks, the performance of the components is also tested and visualized. Comparing with other state-of-the-art methods, the proposed approach shows superiority. [ABSTRACT FROM AUTHOR]

Recent advances in graph convolutional networks (GCNs), which mainly focus on how to exploit information from different hops of neighbors in an efficient way, have brought substantial improvement to many graph data modeling tasks. Most of the existing GCN-based models however are built on the basis of a fixed adjacency matrix, i.e., a single view topology of the underlying graph. That inherently limits the expressive power of the developed models especially when the raw graphs are often noisy or even incomplete due to the inevitably error-prone data measurement or collection. In this paper, we propose a novel framework, termed Multi-View Graph Convolutional Networks with Attention Mechanism (MAGCN), by incorporating multiple views of topology and an attention-based feature aggregation strategy into the computation of graph convolution. As an advanced variant of GCNs, MAGCN is fed with multiple "trustable" topologies, which already exist for a given task or are empirically generated by some classical graph construction methods, which has good potential to produce a better learning representation for downstream tasks. Furthermore, we present some theoretical analysis about the expressive power and flexibility of MAGCN, which provides a general explanation as to why multi-view based methods can potentially outperform those relying on a single view. Our experimental study demonstrates the state-of-the-art accuracies of MAGCN on Cora, Citeseer, and Pubmed datasets. Robustness analysis is also undertaken to show the advantage of MAGCN in handling some uncertainty issues in node classification tasks. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs), with their promising potential to learn effective graph representation, have been widely used for recommender systems, in which the given graph data contains abundant users, items, and their historical interaction information. How to obtain preferable latent representations for both users and items is one of the key issues for GNN-based recommendation. This paper develops a novel deep GNN model with multi-graph attention fusion, MAF-GNN. This framework constructs two feature graph attention modules and a multi-scale latent features module, to generate better user and item latent features from input information. Specifically, the dual-branch residual graph attention (DBRGA) module is presented to extract neighbors' similar features from user and item graphs effectively and easily. Then multi-scale latent matrices are captured by applying non-linear transformations which are embedded to reduce the cost of dimension selection. Furthermore, a hybrid fusion graph attention (HFGA) module is designed to obtain valuable collaborative information from the user–item interaction graph, aiming to further refine the latent embedding of users and items. Finally, the whole MAF-GNN framework is optimized by a geometric factorized regularization loss. Extensive experiment results on both synthetic and real-world datasets illustrate that MAF-GNN can achieve better recommendation performance with a certain level of interpretability than some existing approaches. • A novel deep GNN model with multi-graph attention fusion mechanism is proposed. • A dual-branch residual graph attention module is developed. • A hybrid fusion graph attention module is designed to obtain valuable information. • Multi-scale latent matrices are designed to reduce significantly the time cost. [ABSTRACT FROM AUTHOR]

In point cloud, some regions typically exist nodes from multiple categories, i.e., these regions have both homophilic and heterophilic nodes. However, most existing methods ignore the heterophily of edges during the aggregation of the neighborhood node features, which inevitably mixes unnecessary information of heterophilic nodes and leads to blurred boundaries of segmentation. To address this problem, we model the point cloud as a homophilic-heterophilic graph and propose a graph regulation network (GRN) to produce finer segmentation boundaries. The proposed method can adaptively adjust the propagation mechanism with the degree of neighborhood homophily. Moreover, we build a prototype feature extraction module, which is utilised to mine the homophily features of nodes from the global prototype space. Theoretically, we prove that our convolution operation can constrain the similarity of representations between nodes based on their degree of homophily. Extensive experiments on fully and weakly supervised point cloud semantic segmentation tasks demonstrate that our method achieves satisfactory performance. Especially in the case of weak supervision, that is, each sample has only 1%-10% labeled points, the proposed method has a significant improvement in segmentation performance.

Deep learning-based methods have been widely used in medical image segmentation recently. However, existing works are usually difficult to simultaneously capture global long-range information from images and topological correlations among feature maps. Further, medical images often suffer from blurred target edges. Accordingly, this paper proposes a novel medical image segmentation framework named a label-decoupled network with spatial-channel graph convolution and dual attention enhancement mechanism (LADENet for short). It constructs learnable adjacency matrices and utilizes graph convolutions to effectively capture global long-range information on spatial locations and topological dependencies between different channels in an image. Then a label-decoupled strategy based on distance transformation is introduced to decouple an original segmentation label into a body label and an edge label for supervising the body branch and edge branch. Again, a dual attention enhancement mechanism, designing a body attention block in the body branch and an edge attention block in the edge branch, is built to promote the learning ability of spatial region and boundary features. Besides, a feature interactor is devised to fully consider the information interaction between the body and edge branches to improve segmentation performance. Experiments on benchmark datasets reveal the superiority of LADENet compared to state-of-the-art approaches.

Although convolutional neural networks (CNNs) have shown good performance on grid data, they are limited in the semantic segmentation of irregular point clouds. This article proposes a novel and effective graph CNN framework, referred to as the local-global graph convolutional method (LGGCM), which can achieve short- and long-range dependencies on point clouds. The key to this framework is the design of local spatial attention convolution (LSA-Conv). The design includes two parts: generating a weighted adjacency matrix of the local graph composed of neighborhood points, and updating and aggregating the features of nodes to obtain the spatial geometric features of the local point cloud. In addition, a smooth module for central points is incorporated into the process of LSA-Conv to enhance the robustness of the convolution against noise interference by adjusting the position coordinates of the points adaptively. The learned robust LSA-Conv features are then fed into a global spatial attention module with the gated unit to extract long-range contextual information and dynamically adjust the weights of features from different stages. The proposed framework, consisting of both encoding and decoding branches, is an end-to-end trainable network for semantic segmentation of 3-D point clouds. The theoretical analysis of the approximation capabilities of LSA-Conv is discussed to determine whether the features of the point cloud can be accurately represented. Experimental results on challenging benchmarks of the 3-D point cloud demonstrate that the proposed framework achieves excellent performance.

Graph convolutional neural networks can effectively process geometric data and thus have been successfully used in point cloud data representation. However, existing graph-based methods usually adopt the K-nearest neighbor (KNN) algorithm to construct graphs, which may not be optimal for point cloud analysis tasks, owning to the solution of KNN is independent of network training. In this paper, we propose a novel graph structure learning convolutional neural network (GSLCN) for multiple point cloud analysis tasks. The fundamental concept is to propose a general graph structure learning architecture (GSL) that builds long-range and short-range dependency graphs. To learn optimal graphs that best serve to extract local features and investigate global contextual information, respectively, we integrated the GSL with the designed graph convolution operator under a unified framework. Furthermore, we design the graph structure losses with some prior knowledge to guide graph learning during network training. The main benefit is that given labels and prior knowledge are taken into account in GSLCN, providing useful supervised information to build graphs and thus facilitating the graph convolution operation for the point cloud. Experimental results on challenging benchmarks demonstrate that the proposed framework achieves excellent performance for point cloud classification, part segmentation, and semantic segmentation.

Graph neural networks (GNNs) have shown strong graph-structured data processing capabilities. However, most of them are generated based on the message-passing mechanism and lack of the systematic approach to guide their developments. Meanwhile, a unified point of view is hard to explain the design concepts of different GNN models. This paper presents a unified optimization framework from hybrid regularized graph signal reconstruction to establish the connection between the aggregation operations of different GNNs, showing that exploring the optimal solution is the process of GNN information aggregation. We use this new framework to mathematically explain several classic GNN models and summarizes their commonalities and differences from a macro perspective. The proposed framework not only provides convenience to understand GNNs, but also has a guiding significance for the proposal of new GNNs. Moreover, we design a model-driven fixed-point iteration method and a data-driven dictionary learning network according to the corresponding optimization objective and sparse representation. Then the new model, GNN based on model-driven and data-driven (GNN-MD), is established by using alternating iteration methods. We also theoretically analyze its convergence. Numerous node classification experiments on multiple datasets illustrate that the proposed GNN-MD has excellent performance and outperforms all baselines on high-feature-dimension datasets., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

This study builds a fully deconvolutional neural network (FDNN) and addresses the problem of single image super-resolution (SISR) by using the FDNN. Although SISR using deep neural networks has been a major research focus, the problem of reconstructing a high resolution (HR) image with an FDNN has received little attention. A few recent approaches toward SISR are to embed deconvolution operations into multilayer feedforward neural networks. This paper constructs a deep FDNN for SISR that possesses two remarkable advantages compared to existing SISR approaches. The first improves the network performance without increasing the depth of the network or embedding complex structures. The second replaces all convolution operations with deconvolution operations to implement an effective reconstruction. That is, the proposed FDNN only contains deconvolution layers and learns an end-to-end mapping from low resolution (LR) to HR images. Furthermore, to avoid the oversmoothness of the mean squared error loss, the trained image is treated as a probability distribution, and the Kullback-Leibler divergence is introduced into the final loss function to achieve enhanced recovery. Although the proposed FDNN only has 10 layers, it is successfully evaluated through extensive experiments. Compared with other state-of-the-art methods and deep convolution neural networks with 20 or 30 layers, the proposed FDNN achieves better performance for SISR., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

In recent years, convolutional neural networks have been successfully applied to single image super-resolution (SISR) tasks, making breakthrough progress both in accuracy and speed. In this work, an improved dual-scale residual network (IDSRN), achieving promising reconstruction performance without sacrificing too much calculations, is proposed for SISR. The proposed network extracts features through two independent parallel branches: dual-scale feature extraction branch and texture attention branch. The improved dual-scale residual block (IDSRB) combined with active weighted mapping strategy constitutes the dual-scale feature extraction branch, which aims to capture dual-scale features of the image. As regards the texture attention branch, an encoder-decoder network employing symmetric full convolutional-deconvolution structure acts as a feature selector to enhance the high-frequency details. The integration of two branches reaches the goal of capturing dual-scale features with high-frequency information. Comparative experiments and extensive studies indicate that the proposed IDSRN can catch up with the state-of-the-art approaches in terms of accuracy and efficiency., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

It is well known that the support vector machine (SVM) is an effective learning algorithm. The alternating direction method of multipliers (ADMM) algorithm has emerged as a powerful technique for solving distributed optimisation models. This paper proposes a distributed SVM algorithm in a master-slave mode (MS-DSVM), which integrates a distributed SVM and ADMM acting in a master-slave configuration where the master node and slave nodes are connected, meaning the results can be broadcasted. The distributed SVM is regarded as a regularised optimisation problem and modelled as a series of convex optimisation sub-problems that are solved by ADMM. Additionally, the over-relaxation technique is utilised to accelerate the convergence rate of the proposed MS-DSVM. Our theoretical analysis demonstrates that the proposed MS-DSVM has linear convergence, meaning it possesses the fastest convergence rate among existing standard distributed ADMM algorithms. Numerical examples demonstrate that the convergence and accuracy of the proposed MS-DSVM are superior to those of existing methods under the ADMM framework., (Copyright © 2018 Elsevier Ltd. All rights reserved.)