57 citations · 82 across the 3 of their papers we have counts for
3 papers
cs.CV2024
BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-Resolution
Feng Li, Yixuan Wu, Zichao Liang +4
Diffusion models (DM) have achieved remarkable promise in image super-resolution (SR). However, most of them are tailored to solving non-blind inverse problems with fixed known deg…
cs.CV2022★ 25 cited
Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution
Yixuan Wu, Feng Li, Huihui Bai +3
Convolutional Neural Network (CNN)-based image super-resolution (SR) has exhibited impressive success on known degraded low-resolution (LR) images. However, this type of approach i…
cs.CV2022★ 57 cited
Learning Detail-Structure Alternative Optimization for Blind Super-Resolution
Feng Li, Yixuan Wu, Huihui Bai +3
Existing convolutional neural networks (CNN) based image super-resolution (SR) methods have achieved impressive performance on bicubic kernel, which is not valid to handle unknown…