151 citations · 227 across the 12 of their papers we have counts for
13 papers
HST: Hierarchical Swin Transformer for Compressed Image Super-resolution
Bingchen Li, Xin Li, Yiting Lu +3
Compressed Image Super-resolution has achieved great attention in recent years, where images are degraded with compression artifacts and low-resolution artifacts. Since the complex…
RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment
Yiting Lu, Jun Fu, Xin Li +6
Coronary CT Angiography (CCTA) is susceptible to various distortions (e.g., artifacts and noise), which severely compromise the exact diagnosis of cardiovascular diseases. The appr…
FAN: Frequency Aggregation Network for Real Image Super-resolution
Yingxue Pang, Xin Li, Xin Jin +4
Single image super-resolution (SISR) aims to recover the high-resolution (HR) image from its low-resolution (LR) input image. With the development of deep learning, SISR has achiev…
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Pengxu Wei, Hannan Lu, Radu Timofte +68
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This ch…
LIRA: Lifelong Image Restoration from Unknown Blended Distortions
Jianzhao Liu, Jianxin Lin, Xin Li +3
Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task.…
Learning Disentangled Feature Representation for Hybrid-distorted Image Restoration
Xin Li, Xin Jin, Jianxin Lin +5
Hybrid-distorted image restoration (HD-IR) is dedicated to restore real distorted image that is degraded by multiple distortions. Existing HD-IR approaches usually ignore the inher…