All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation

Understand

The aim of image restoration is to recover high-quality images from distorted ones.

• However, current methods usually focus on a single task (\emph{e.g.}, denoising, deblurring or super-resolution) which cannot address the needs of real-world multi-task processing, especially on mobile devices.
• Thus, developing an all-in-one method that can restore images from various unknown distortions is a significant challenge.
• Previous works have employed contrastive learning to learn the degradation representation from observed images, but this often leads to representation drift caused by deficient positive and negative pairs.