Image restoration is a long-standing low-level vision problem that aims to restore high-
quality images from low-quality images (eg, downscaled, noisy and compressed images) …

Video restoration aims to restore high-quality frames from low-quality frames. Different from
single image restoration, video restoration generally requires to utilize temporal information …

Video restoration aims at restoring multiple high-quality frames from multiple low-quality
frames. Existing video restoration methods generally fall into two extreme cases, ie, they …

The diversity and complexity of degradations in real-world video super-resolution (VSR)
pose non-trivial challenges in inference and training. First, while long-term propagation …

Reference-based image super-resolution (RefSR) aims to exploit auxiliary reference (Ref)
images to super-resolve low-resolution (LR) images. Recently, RefSR has been attracting …

In various learning-based image restoration tasks, such as image denoising and image
super-resolution, the degradation representations were widely used to model the …

Normalizing flows have recently demonstrated promising results for low-level vision tasks.
For image super-resolution (SR), it learns to predict diverse photo-realistic high-resolution …

Learning continuous image representations is recently gaining popularity for image super-
resolution (SR) because of its ability to reconstruct high-resolution images with arbitrary …

Translational displacement between source images from different sensors is a general
phenomenon, which will cause performance degradation on image fusion. To tackle this …

Learning based approaches have witnessed great successes in blind single image super-
resolution (SISR) tasks, however, handcrafted kernel priors and learning based kernel priors …