Adaptive Blind All-in-One Image Restoration

Understand

Blind all-in-one image restoration models aim to recover a high-quality image from an input degraded with unknown distortions.

• However, these models require all the possible degradation types to be defined during the training stage while showing limited generalization to unseen degradations, which limits their practical application in complex cases.
• In this paper, we introduce ABAIR, a simple yet effective adaptive blind all-in-one restoration model that not only handles multiple degradations and generalizes well to unseen distortions but also efficiently integrates new degradations by training only a small subset of parameters.
• We first train our baseline model on a large dataset of natural images with multiple synthetic degradations.