Adaptive Blind All-in-One Image Restoration
Authors: name: David Serrano-Lozano, name: Luis Herranz, name: Shaolin Su, name: Javier Vazquez-Corral

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 propose a simple but
effective adaptive blind all-in-one restoration (ABAIR) model, which can
address multiple degradations, generalizes well to unseen degradations, and
efficiently incorporate new degradations by training a small fraction of
parameters. First, we train our baseline model on a large dataset of natural
images with multiple synthetic degradations, augmented with a segmentation head
to estimate per-pixel degradation types, resulting in a powerful backbone able
to generalize to a wide range of degradations. Second, we adapt our baseline
model to varying image restoration tasks using independent low-rank adapters.
Third, we learn to adaptively combine adapters to versatile images via a
flexible and lightweight degradation estimator. Our model is both powerful in
handling specific distortions and flexible in adapting to complex tasks, it not
only outperforms the state-of-the-art by a large margin on five- and three-task
IR setups, but also shows improved generalization to unseen degradations and
also composite distortions.