146 citations · 260 across the 5 of their papers we have counts for
5 papers
End-to-end Alternating Optimization for Real-World Blind Super Resolution
Zhengxiong Luo, Yan Huang, Shang Li +2
Blind Super-Resolution (SR) usually involves two sub-problems: 1) estimating the degradation of the given low-resolution (LR) image; 2) super-resolving the LR image to its high-res…
Learning the Degradation Distribution for Blind Image Super-Resolution
Zhengxiong Luo, Yan Huang, Shang Li +2
Synthetic high-resolution (HR) \& low-resolution (LR) pairs are widely used in existing super-resolution (SR) methods. To avoid the domain gap between synthetic and test images, mo…
From General to Specific: Online Updating for Blind Super-Resolution
Shang Li, Guixuan Zhang, Zhengxiong Luo +3
Most deep learning-based super-resolution (SR) methods are not image-specific: 1) They are trained on samples synthesized by predefined degradations (e.g. bicubic downsampling), re…
End-to-end Alternating Optimization for Blind Super Resolution
Zhengxiong Luo, Yan Huang, Shang Li +2
Previous methods decompose the blind super-resolution (SR) problem into two sequential steps: \textit{i}) estimating the blur kernel from given low-resolution (LR) image and \texti…
Unfolding the Alternating Optimization for Blind Super Resolution
Zhengxiong Luo, Yan Huang, Shang Li +2
Previous methods decompose blind super resolution (SR) problem into two sequential steps: \textit{i}) estimating blur kernel from given low-resolution (LR) image and \textit{ii}) r…