activity
20182022
most citedSwinIR: Image Restoration Using Swin Transformer

81 citations · 108 across the 8 of their papers we have counts for

collaborators

16 papers

cs.CV2022

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

Yawei Li, Kai Zhang, Radu Timofte +108

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-reso…

cs.CV2022

NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

Ren Yang, Radu Timofte, Meisong Zheng +75

This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the…

eess.IV2021

Towards Flexible Blind JPEG Artifacts Removal

Jiaxi Jiang, Kai Zhang, Radu Timofte

Training a single deep blind model to handle different quality factors for JPEG image artifacts removal has been attracting considerable attention due to its convenience for practi…

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…