328 citations · 633 across the 23 of their papers we have counts for
21 papers · 1 filter
-PnP: Plug-and-Play Denoising for Scientific Imaging with Self-Supervised Score Priors
Yukun Cui, Jiangshe Zhang, Shuang Xu +6
Modern image restoration methods increasingly rely on large-scale supervised training, yet clean targets are often prohibitively difficult to obtain in scientific imaging modalitie…
A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarking
Zixiang Zhao, Haowen Bai, Bingxin Ke +5
The real world is dynamic, yet most image fusion methods process static frames independently, ignoring temporal correlations in videos and leading to flickering and temporal incons…
Retinex-MEF: Retinex-based Glare Effects Aware Unsupervised Multi-Exposure Image Fusion
Haowen Bai, Jiangshe Zhang, Zixiang Zhao +3
Multi-exposure image fusion (MEF) synthesizes multiple, differently exposed images of the same scene into a single, well-exposed composite. Retinex theory, which separates image il…
Simultaneous Automatic Picking and Manual Picking Refinement for First-Break
Haowen Bai, Zixiang Zhao, Jiangshe Zhang +4
First-break picking is a pivotal procedure in processing microseismic data for geophysics and resource exploration. Recent advancements in deep learning have catalyzed the evolutio…
Deep Unfolding Multi-modal Image Fusion Network via Attribution Analysis
Haowen Bai, Zixiang Zhao, Jiangshe Zhang +5
Multi-modal image fusion synthesizes information from multiple sources into a single image, facilitating downstream tasks such as semantic segmentation. Current approaches primaril…
Hipandas: Hyperspectral Image Joint Denoising and Super-Resolution by Image Fusion with the Panchromatic Image
Shuang Xu, Zixiang Zhao, Haowen Bai +4
Hyperspectral images (HSIs) are frequently noisy and of low resolution due to the constraints of imaging devices. Recently launched satellites can concurrently acquire HSIs and pan…
Task-driven Image Fusion with Learnable Fusion Loss
Haowen Bai, Jiangshe Zhang, Zixiang Zhao +5
Multi-modal image fusion aggregates information from multiple sensor sources, achieving superior visual quality and perceptual features compared to single-source images, often impr…
Image Fusion via Vision-Language Model
Zixiang Zhao, Lilun Deng, Haowen Bai +8
Image fusion integrates essential information from multiple images into a single composite, enhancing structures, textures, and refining imperfections. Existing methods predominant…
ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning
Haowen Bai, Zixiang Zhao, Jiangshe Zhang +5
Image fusion aims to combine information from multiple source images into a single one with more comprehensive informational content. Deep learning-based image fusion algorithms fa…
Neural Gradient Regularizer
Shuang Xu, Yifan Wang, Zixiang Zhao +6
Owing to its significant success, the prior imposed on gradient maps has consistently been a subject of great interest in the field of image processing. Total variation (TV), one o…
Equivariant Multi-Modality Image Fusion
Zixiang Zhao, Haowen Bai, Jiangshe Zhang +6
Multi-modality image fusion is a technique that combines information from different sensors or modalities, enabling the fused image to retain complementary features from each modal…
Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution
Zixiang Zhao, Jiangshe Zhang, Xiang Gu +5
Guided depth map super-resolution (GDSR), as a hot topic in multi-modal image processing, aims to upsample low-resolution (LR) depth maps with additional information involved in hi…
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
Zixiang Zhao, Haowen Bai, Yuanzhi Zhu +7
Multi-modality image fusion aims to combine different modalities to produce fused images that retain the complementary features of each modality, such as functional highlights and…
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…
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…
FGF-GAN: A Lightweight Generative Adversarial Network for Pansharpening via Fast Guided Filter
Zixiang Zhao, Jiangshe Zhang, Shuang Xu +4
Pansharpening is a widely used image enhancement technique for remote sensing. Its principle is to fuse the input high-resolution single-channel panchromatic (PAN) image and low-re…
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…
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…