328 citations · 706 across the 19 of their papers we have counts for
19 papers
CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion
Zixiang Zhao, Haowen Bai, Jiangshe Zhang +5
Multi-modality (MM) image fusion aims to render fused images that maintain the merits of different modalities, e.g., functional highlight and detailed textures. To tackle the chall…
MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation Picking Network
Hongtao Wang, Jiangshe Zhang, Xiaoli Wei +4
Picking the first arrival times of prestack gathers is called First Arrival Time (FAT) picking, which is an indispensable step in seismic data processing, and is mainly solved manu…
Understanding Short-Range Memory Effects in Deep Neural Networks
Chengli Tan, Jiangshe Zhang, Junmin Liu
Stochastic gradient descent (SGD) is of fundamental importance in deep learning. Despite its simplicity, elucidating its efficacy remains challenging. Conventionally, the success o…
Discrete Cosine Transform Network for Guided Depth Map Super-Resolution
Zixiang Zhao, Jiangshe Zhang, Shuang Xu +2
Guided depth super-resolution (GDSR) is an essential topic in multi-modal image processing, which reconstructs high-resolution (HR) depth maps from low-resolution ones collected wi…
Deep Convolutional Sparse Coding Network for Pansharpening with Guidance of Side Information
Shuang Xu, Jiangshe Zhang, Kai Sun +4
Pansharpening is a fundamental issue in remote sensing field. This paper proposes a side information partially guided convolutional sparse coding (SCSC) model for pansharpening. Th…
Deep Gradient Projection Networks for Pan-sharpening
Shuang Xu, Jiangshe Zhang, Zixiang Zhao +3
Pan-sharpening is an important technique for remote sensing imaging systems to obtain high resolution multispectral images. Recently, deep learning has become the most popular tool…
Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy
Shuang Xu, Lizhen Ji, Zhe Wang +4
Multi-focus image fusion (MFF) is a popular technique to generate an all-in-focus image, where all objects in the scene are sharp. However, existing methods pay little attention to…
Domain Adaptive Object Detection via Feature Separation and Alignment
Chengyang Liang, Zixiang Zhao, Junmin Liu +1
Recently, adversarial-based domain adaptive object detection (DAOD) methods have been developed rapidly. However, there are two issues that need to be resolved urgently. Firstly, n…
MFIF-GAN: A New Generative Adversarial Network for Multi-Focus Image Fusion
Yicheng Wang, Shuang Xu, Junmin Liu +3
Multi-Focus Image Fusion (MFIF) is a promising image enhancement technique to obtain all-in-focus images meeting visual needs and it is a precondition of other computer vision task…
When Image Decomposition Meets Deep Learning: A Novel Infrared and Visible Image Fusion Method
Zixiang Zhao, Jiangshe Zhang, Shuang Xu +3
Infrared and visible image fusion, as a hot topic in image processing and image enhancement, aims to produce fused images retaining the detail texture information in visible images…
Deep Convolutional Sparse Coding Networks for Image Fusion
Shuang Xu, Zixiang Zhao, Yicheng Wang +3
Image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few. Recently, deep learning has emerged…
Bayesian Fusion for Infrared and Visible Images
Zixiang Zhao, Shuang Xu, Chunxia Zhang +2
Infrared and visible image fusion has been a hot issue in image fusion. In this task, a fused image containing both the gradient and detailed texture information of visible images…
Efficient and Model-Based Infrared and Visible Image Fusion Via Algorithm Unrolling
Zixiang Zhao, Shuang Xu, Jiangshe Zhang +3
Infrared and visible image fusion (IVIF) expects to obtain images that retain thermal radiation information from infrared images and texture details from visible images. In this pa…
DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion
Zixiang Zhao, Shuang Xu, Chunxia Zhang +3
Infrared and visible image fusion, a hot topic in the field of image processing, aims at obtaining fused images keeping the advantages of source images. This paper proposes a novel…
MFFW: A new dataset for multi-focus image fusion
Shuang Xu, Xiaoli Wei, Chunxia Zhang +2
Multi-focus image fusion (MFF) is a fundamental task in the field of computational photography. Current methods have achieved significant performance improvement. It is found that…
Toward a Controllable Disentanglement Network
Zengjie Song, Oluwasanmi Koyejo, Jiangshe Zhang
This paper addresses two crucial problems of learning disentangled image representations, namely controlling the degree of disentanglement during image editing, and balancing the d…
Learning Controllable Disentangled Representations with Decorrelation Regularization
Zengjie Song, Oluwasanmi Koyejo, Jiangshe Zhang
A crucial problem in learning disentangled image representations is controlling the degree of disentanglement during image editing, while preserving the identity of objects. In thi…
Adaptive Quantile Low-Rank Matrix Factorization
Shuang Xu, Chun-Xia Zhang, Jiangshe Zhang
Low-rank matrix factorization (LRMF) has received much popularity owing to its successful applications in both computer vision and data mining. By assuming noise to come from a Gau…
Variational Bayesian Weighted Complex Network Reconstruction
Shuang Xu, Chun-Xia Zhang, Pei Wang +1
Complex network reconstruction is a hot topic in many fields. Currently, the most popular data-driven reconstruction framework is based on lasso. However, it is found that, in the…