activity
20212025
most citedSwinIR: Image Restoration Using Swin Transformer

81 citations · 99 across the 7 of their papers we have counts for

collaborators

9 papers

cs.CV2025

Low-Light Image Enhancement using Event-Based Illumination Estimation

Lei Sun, Yuhan Bao, Jiajun Zhai +5

Low-light image enhancement (LLIE) aims to improve the visibility of images captured in poorly lit environments. Prevalent event-based solutions primarily utilize events triggered…

cs.CV2025

Fractal-IR: A Unified Framework for Efficient and Scalable Image Restoration

Yawei Li, Bin Ren, Jingyun Liang +5

While vision transformers achieve significant breakthroughs in various image restoration (IR) tasks, it is still challenging to efficiently scale them across multiple types of degr…

cs.CV20241 cited

Hierarchical Information Flow for Generalized Efficient Image Restoration

Yawei Li, Bin Ren, Jingyun Liang +5

While vision transformers show promise in numerous image restoration (IR) tasks, the challenge remains in efficiently generalizing and scaling up a model for multiple IR tasks. To…

eess.IV202181 cited

SwinIR: Image Restoration Using Swin Transformer

Jingyun Liang, Jiezhang Cao, Guolei Sun +3

Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). Whil…

cs.CV20213 cited

Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution

Jingyun Liang, Guolei Sun, Kai Zhang +2

Existing blind image super-resolution (SR) methods mostly assume blur kernels are spatially invariant across the whole image. However, such an assumption is rarely applicable for r…

eess.IV20211 cited

Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling

Jingyun Liang, Andreas Lugmayr, Kai Zhang +3

Normalizing flows have recently demonstrated promising results for low-level vision tasks. For image super-resolution (SR), it learns to predict diverse photo-realistic high-resolu…