Official PyTorch implementation of the paper "Deep Constrained Least Squares for Blind Image Super-Resolution", CVPR 2022.

Official PyTorch code for Flow-based Kernel Prior with Application to Blind Super-Resolution (FKP, CVPR2021)

This project is the official implementation of 'Knowledge Distillation based Degradation Estimation for Blind Super-Resolution', ICLR2023

Asymmetric CNN for image super-resolution (IEEE Transactions on Systmes, Man, and Cybernetics: Systems 2021)

Official PyTorch Implementation of "StarSRGAN: Improving Real-World Blind Super-Resolution" (WSCG 2023)

Open source image super-resolution toolbox containing various functionality for training a diverse number of state-of-the-art deep learning models. Also acts as the companion code for the MDPI Sensors journal paper titled 'The Best of Both Worlds: A Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks'

This project is the official implementation of 'Meta-Learning based Degradation Representation for Blind Super-Resolution', TIP2023

Official PyTorch implementation of the paper "Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images", IEEE TIM2024. CSBSR is an advanced version of our previous work CSSR [MVA'21].

Blind Super Resolution based on USRNet and Kernel GAN

A pytorch implementation of Learning Detail-Structure Alternative Optimization for Blind Super-Resolution.

Official implementation of "Pixel-level Kernel Estimation for Blind Super-Resolution", IEEE Access 2021.

IKC: Blind Super-Resolution With Iterative Kernel Correction

When photographing a light source with a smartphone camera, light smudging often occurs. Our model contributes to improving the image quality degraded by the spread of light around the light source.