activity
20202025
most citedYou Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network

14 citations · 14 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CV2025

LaverNet: Lightweight All-in-one Video Restoration via Selective Propagation

Haiyu Zhao, Yiwen Shan, Yuanbiao Gou +1

Recent studies have explored all-in-one video restoration, which handles multiple degradations with a unified model. However, these approaches still face two challenges when dealin…

cs.CV2025

Next-Scale Prediction: A Self-Supervised Approach for Real-World Image Denoising

Yiwen Shan, Haiyu Zhao, Peng Hu +2

Self-supervised real-world image denoising remains a fundamental challenge, arising from the antagonistic trade-off between decorrelating spatially structured noise and preserving…

cs.CV2024

MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration

Boyun Li, Haiyu Zhao, Wenxin Wang +3

Recent advancements in Mamba have shown promising results in image restoration. These methods typically flatten 2D images into multiple distinct 1D sequences along rows and columns…

cs.LG2024

Conditional Distribution Learning for Graph Classification

Jie Chen, Hua Mao, Chuanbin Liu +2

Leveraging the diversity and quantity of data provided by various graph-structured data augmentations while preserving intrinsic semantic information is challenging. Additionally,…

cs.LG2024

Hierarchical Sparse Representation Clustering for High-Dimensional Data Streams

Jie Chen, Hua Mao, Yuanbiao Gou +1

Data stream clustering reveals patterns within continuously arriving, potentially unbounded data sequences. Numerous data stream algorithms have been proposed to cluster data strea…

cs.CV202014 cited

You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network

Boyun Li, Yuanbiao Gou, Shuhang Gu +3

In this paper, we study two challenging and less-touched problems in single image dehazing, namely, how to make deep learning achieve image dehazing without training on the ground-…