activity
20202026
most citedDual Adversarial Network: Toward Real-world Noise Removal and Noise Generation

14 citations · 19 across the 6 of their papers we have counts for

collaborators

10 papers

cs.CV2026

Enhancing Underwater Light Field Images via Global Geometry-aware Diffusion Process

Yuji Lin, Qian Zhao, Zongsheng Yue +2

This work studies the challenging problem of acquiring high-quality underwater images via 4-D light field (LF) imaging. To this end, we propose GeoDiff-LF, a novel diffusion-based…

cs.CV2025

Generative Latent Kernel Modeling for Blind Motion Deblurring

Chenhao Ding, Jiangtao Zhang, Zongsheng Yue +3

Deep prior-based approaches have demonstrated remarkable success in blind motion deblurring (BMD) recently. These methods, however, are often limited by the high non-convexity of t…

cs.CV2024

Blind Image Deconvolution by Generative-based Kernel Prior and Initializer via Latent Encoding

Jiangtao Zhang, Zongsheng Yue, Hui Wang +2

Blind image deconvolution (BID) is a classic yet challenging problem in the field of image processing. Recent advances in deep image prior (DIP) have motivated a series of DIP-base…

cs.CV2023

ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting

Zongsheng Yue, Jianyi Wang, Chen Change Loy

Diffusion-based image super-resolution (SR) methods are mainly limited by the low inference speed due to the requirements of hundreds or even thousands of sampling steps. Existing…

cs.CV2023

Unsupervised Hyperspectral Pansharpening via Low-rank Diffusion Model

Xiangyu Rui, Xiangyong Cao, Li Pang +3

Hyperspectral pansharpening is a process of merging a high-resolution panchromatic (PAN) image and a low-resolution hyperspectral (LRHS) image to create a single high-resolution hy…

cs.CV2023

Exploiting Diffusion Prior for Real-World Image Super-Resolution

Jianyi Wang, Zongsheng Yue, Shangchen Zhou +2

We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution (SR). Specifically, by employing our t…