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View article: MCNN: Conditional focus probability learning to multi‐focus image fusion via mutually coupled neural network
MCNN: Conditional focus probability learning to multi‐focus image fusion via mutually coupled neural network Open
In this paper, a novel conditional focus probability learning model, termed MCNN, is proposed for multi‐focus image fusion (MFIF). Given a pair of source images, their conditional focus probabilities can be generated by using the well‐trai…
View article: GAGCN: Generative adversarial graph convolutional network for non‐homogeneous texture extension synthesis
GAGCN: Generative adversarial graph convolutional network for non‐homogeneous texture extension synthesis Open
In the non‐homogeneous texture synthesis task, the overall visual characteristics should be consistent when extending the local patterns of the exemplar. The existing methods mainly focus on the local visual features of patterns but ignore…
View article: MPEFNet: Multilevel Progressive Enhancement Fusion Network for Pansharpening
MPEFNet: Multilevel Progressive Enhancement Fusion Network for Pansharpening Open
Remote sensing image fusion is a key technique to fuse low spatial resolution multispectral (MS) images with high spatial resolution panchromatic (PAN) images to obtain high spatial resolution multispectral images. However, many existing f…
View article: DPAFNet: A Multistage Dense-Parallel Attention Fusion Network for Pansharpening
DPAFNet: A Multistage Dense-Parallel Attention Fusion Network for Pansharpening Open
Pansharpening is the technology to fuse a low spatial resolution MS image with its associated high spatial full resolution PAN image. However, primary methods have the insufficiency of the feature expression and do not explore both the int…
View article: LP-BFGS attack: An adversarial attack based on the Hessian with limited pixels
LP-BFGS attack: An adversarial attack based on the Hessian with limited pixels Open
Deep neural networks are vulnerable to adversarial attacks. Most $L_{0}$-norm based white-box attacks craft perturbations by the gradient of models to the input. Since the computation cost and memory limitation of calculating the Hessian m…
View article: EDAfuse: A encoder–decoder with atrous spatial pyramid network for infrared and visible image fusion
EDAfuse: A encoder–decoder with atrous spatial pyramid network for infrared and visible image fusion Open
Infrared and visible images come from different sensors, and they have their advantages and disadvantages. In order to make the fused images contain as much salience information as possible, a practical fusion method, termed EDAfuse, is pr…
View article: SSL-WAEIE: Self-Supervised Learning With Weighted Auto-Encoding and Information Exchange for Infrared and Visible Image Fusion
SSL-WAEIE: Self-Supervised Learning With Weighted Auto-Encoding and Information Exchange for Infrared and Visible Image Fusion Open
Dear editor, Infrared and visible image fusion (IVIF) technologies are to extract complementary information from source images and generate a single fused result [1], which is widely applied in various high-level visual tasks such as segme…
View article: Exploring Adversarial Examples and Adversarial Robustness of Convolutional Neural Networks by Mutual Information
Exploring Adversarial Examples and Adversarial Robustness of Convolutional Neural Networks by Mutual Information Open
A counter-intuitive property of convolutional neural networks (CNNs) is their inherent susceptibility to adversarial examples, which severely hinders the application of CNNs in security-critical fields. Adversarial examples are similar to …
View article: CEFusion: Multi‐Modal medical image fusion via cross encoder
CEFusion: Multi‐Modal medical image fusion via cross encoder Open
Most existing deep learning‐based multi‐modal medical image fusion (MMIF) methods utilize single‐branch feature extraction strategies to achieve good fusion performance. However, for MMIF tasks, it is thought that this structure cuts off t…
View article: Meta Reinforcement Learning Based Computation Offloading Strategy for Vehicular Networks
Meta Reinforcement Learning Based Computation Offloading Strategy for Vehicular Networks Open
Deep learning (DL) and reinforcement learning (RL) based methods can efficiently generate offloading strategies for computational offloading problems in mobile edge computing (MEC) environments. However, the rapid movement of vehicles in t…
View article: LineGAN: An image colourisation method combined with a line art network
LineGAN: An image colourisation method combined with a line art network Open
The work on grayscale image colourisation has been significantly improved. Currently, learning‐based methods have achieved some great colourisation effects, but existing colour edge bleeding, especially when colourful cartoon characters. I…
View article: Trident‐YOLO: Improving the precision and speed of mobile device object detection
Trident‐YOLO: Improving the precision and speed of mobile device object detection Open
This paper introduce an efficient object detection network named Trident‐You Only Look Once (YOLO), which is designed for mobile devices with limited computing power. The new architecture is improved based on YOLO v4‐tiny. The authors rede…
View article: AMBCR: Low‐light image enhancement via attention guided multi‐branch construction and Retinex theory
AMBCR: Low‐light image enhancement via attention guided multi‐branch construction and Retinex theory Open
Due to different lighting environments and equipment limitations, low‐light images have high noise, low contrast and unobvious colours. The main purpose of low‐light image enhancement is to preserve the details and suppress noise as much a…
View article: Construction of high dynamic range image based on gradient information transformation
Construction of high dynamic range image based on gradient information transformation Open
This study proposes a fusion method for high dynamic range images based on gradient information transformation. In the proposed work, the authors first measure the three exposure weights of the source images, namely, local contrast, lumina…
View article: Brain Medical Image Fusion Based on Dual‐Branch CNNs in NSST Domain
Brain Medical Image Fusion Based on Dual‐Branch CNNs in NSST Domain Open
Computed tomography (CT) images show structural features, while magnetic resonance imaging (MRI) images represent brain tissue anatomy but do not contain any functional information. How to effectively combine the images of the two modes ha…
View article: Machine Learning Model Comparison for Automatic Segmentation ofIntracoronary Optical Coherence Tomography and Plaque Cap ThicknessQuantification
Machine Learning Model Comparison for Automatic Segmentation ofIntracoronary Optical Coherence Tomography and Plaque Cap ThicknessQuantification Open
Optical coherence tomography (OCT) is a new intravascular imaging technique with high resolution and could provide accurate morphological information for plaques in coronary arteries. However, its segmentation is still co... | Find, read …
View article: Predictions of Apoptosis Proteins by Integrating Different Features Based on Improving Pseudo‐Position‐Specific Scoring Matrix
Predictions of Apoptosis Proteins by Integrating Different Features Based on Improving Pseudo‐Position‐Specific Scoring Matrix Open
Apoptosis proteins are strongly related to many diseases and play an indispensable role in maintaining the dynamic balance between cell death and division in vivo . Obtaining localization information on apoptosis proteins is necessary in u…
View article: Convolution Neural Networks and Support Vector Machines for Automatic Segmentation of Intracoronary Optical Coherence Tomography
Convolution Neural Networks and Support Vector Machines for Automatic Segmentation of Intracoronary Optical Coherence Tomography Open
Cardiovascular diseases are closely associated with deteriorating atherosclerotic plaques. Optical coherence tomography (OCT) is a recently developed intravascular imaging technique with high resolution approximately 10 micro... | Find, re…
View article: Convolution Neural Networks and Support Vector Machines for Automatic Segmentation of Intracoronary Optical Coherence Tomography
Convolution Neural Networks and Support Vector Machines for Automatic Segmentation of Intracoronary Optical Coherence Tomography Open
Cardiovascular diseases are closely associated with deteriorating atherosclerotic plaques. Optical coherence tomography (OCT) is a recently developed intravascular imaging technique with high resolution approximately 10 micro... | Find, re…
View article: Pulse coupled neural network based MRI image enhancement using classical visual receptive field for smarter mobile healthcare
Pulse coupled neural network based MRI image enhancement using classical visual receptive field for smarter mobile healthcare Open
With the rapid growth of medical big data, medical signal processing measurement techniques are facing severe challenges. Enormous medical images are constantly generated by various health monitoring and sensing devices, such as ultrasound…
View article: Medical image fusion based on variational and nonlinear structure tensor
Medical image fusion based on variational and nonlinear structure tensor Open
Medical image fusion plays an important role in detection and treatment of disease. Although numerous medical image fusion methods have been proposed, most of them decrease the contrast and lose the image information. In this paper, a nove…
View article: A Color Multi-Exposure Image Fusion Approach Using Structural Patch Decomposition
A Color Multi-Exposure Image Fusion Approach Using Structural Patch Decomposition Open
A novel color multi-exposure image fusion approach is proposed to solve the problem of the loss of visual details and vivid colors. The proposed method is based on an image patch that is decomposed into three different independent parts: c…
View article: Infrared and Visible Image Fusion Combining Interesting Region Detection and Nonsubsampled Contourlet Transform
Infrared and Visible Image Fusion Combining Interesting Region Detection and Nonsubsampled Contourlet Transform Open
The most fundamental purpose of infrared (IR) and visible (VI) image fusion is to integrate the useful information and produce a new image which has higher reliability and understandability for human or computer vision. In order to better …