Y
Yiqun Mei
Researcher at University of Illinois at Urbana–Champaign
Publications - 12
Citations - 539
Yiqun Mei is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Computer science & Image restoration. The author has an hindex of 4, co-authored 7 publications receiving 82 citations.
Papers
More filters
Proceedings ArticleDOI
Image Super-Resolution with Non-Local Sparse Attention
Yiqun Mei,Yuchen Fan,Yuqian Zhou +2 more
TL;DR: Non-local sparse attention (NLSA) as mentioned in this paper is designed to retain long-range modeling capability from non-local operation while enjoying robustness and high-efficiency of sparse representation, which partitions the input space into hash buckets of related features.
Proceedings ArticleDOI
Image Super-Resolution With Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
TL;DR: This paper proposes the first Cross-Scale Non-Local (CS-NL) attention module with integration into a recurrent neural network and can find more cross-scale feature correlations within a single low-resolution (LR) image.
Posted Content
Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
TL;DR: Xia et al. as discussed by the authors proposed the first cross-scale non-local attention module with integration into a recurrent neural network to find more crossscale feature correlations within a single low-resolution (LR) image.
Posted Content
Pyramid Attention Networks for Image Restoration
Yiqun Mei,Yuchen Fan,Yulun Zhang,Jiahui Yu,Yuqian Zhou,Ding Liu,Yun Fu,Thomas S. Huang,Humphrey Shi +8 more
TL;DR: A novel Pyramid Attention module for image restoration, which captures long-range feature correspondences from a multi-scale feature pyramid, and is designed to be able to borrow clean signals from their "clean" correspondences at the coarser levels.
Proceedings Article
Neural Sparse Representation for Image Restoration
TL;DR: Experiments show that sparse representation is crucial in deep neural networks for multiple image restoration tasks, including image super-resolution, image denoising, and image compression artifacts removal.