18 citations · 42 across the 10 of their papers we have counts for
7 papers · 1 filter
Learning Subspace-Preserving Sparse Attention Graphs from Heterogeneous Multiview Data
Jie Chen, Yuanbiao Gou, Chuanbin Liu +2
The high-dimensional features extracted from large-scale unlabeled data via various pretrained models with diverse architectures are referred to as heterogeneous multiview data. Mo…
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…
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…
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…
Test-Time Degradation Adaptation for Open-Set Image Restoration
Yuanbiao Gou, Haiyu Zhao, Boyun Li +2
In contrast to close-set scenarios that restore images from a predefined set of degradations, open-set image restoration aims to handle the unknown degradations that were unforesee…
Relationship Quantification of Image Degradations
Wenxin Wang, Boyun Li, Yuanbiao Gou +3
In this paper, we study two challenging but less-touched problems in image restoration, namely, i) how to quantify the relationship between image degradations and ii) how to improv…
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-…