35 citations · 38 across the 2 of their papers we have counts for
4 papers
Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution
Ruofan Zhang, Jinjin Gu, Haoyu Chen +3
Super-resolution (SR) techniques designed for real-world applications commonly encounter two primary challenges: generalization performance and restoration accuracy. We demonstrate…
Masked Image Training for Generalizable Deep Image Denoising
Haoyu Chen, Jinjin Gu, Yihao Liu +5
When capturing and storing images, devices inevitably introduce noise. Reducing this noise is a critical task called image denoising. Deep learning has become the de facto method f…
Rethinking Alignment in Video Super-Resolution Transformers
Shuwei Shi, Jinjin Gu, Liangbin Xie +3
The alignment of adjacent frames is considered an essential operation in video super-resolution (VSR). Advanced VSR models, including the latest VSR Transformers, are generally equ…
Reflash Dropout in Image Super-Resolution
Xiangtao Kong, Xina Liu, Jinjin Gu +2
Dropout is designed to relieve the overfitting problem in high-level vision tasks but is rarely applied in low-level vision tasks, like image super-resolution (SR). As a classic re…