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

TL;DR
ABAIR is an adaptive blind image restoration model that effectively handles multiple and unseen degradations by integrating a segmentation head, low-rank adapters, and a degradation estimator, outperforming state-of-the-art methods.
Contribution
The paper introduces ABAIR, a novel adaptive model that generalizes to unseen distortions and efficiently incorporates new degradations with minimal additional training.
Findings
Surpasses state-of-the-art on multiple IR tasks
Demonstrates strong generalization to unseen distortions
Efficiently adapts to complex degradation scenarios
Abstract
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. To enhance its ability to recognize distortions, we incorporate a segmentation head that estimates per-pixel degradation types. Second, we adapt our…
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Taxonomy
TopicsRandom lasers and scattering media · Advanced Optical Sensing Technologies · Sparse and Compressive Sensing Techniques
