Problem
Research questions and friction points this paper is trying to address.
image restoration
composite degradations
generalization
efficiency
unknown degradations
Restore, Assess, Repeat: A Unified Framework for Iterative Image Restoration
🤖 AI Summary
Existing image restoration methods suffer from limited generalization and low efficiency when handling unknown or composite degradations. To address this, this work proposes the RAR framework, which, for the first time, integrates image quality assessment (IQA) and image restoration (IR) into a unified end-to-end iterative process within a shared latent space, enabling joint optimization of degradation identification, image restoration, and quality verification. The method establishes a dynamic, adaptive all-in-one restoration mechanism that effectively minimizes inter-module information loss and latency. Extensive experiments demonstrate that RAR consistently achieves state-of-the-art performance across scenarios involving single, unknown, and composite degradations.
📝 Abstract
Image restoration aims to recover high quality images from inputs degraded by various factors, such as adverse weather, blur, or low light. While recent studies have shown remarkable progress across individual or unified restoration tasks, they still suffer from limited generalization and inefficiency when handling unknown or composite degradations. To address these limitations, we propose RAR, a Restore, Assess and Repeat process, that integrates Image Quality Assessment (IQA) and Image Restoration (IR) into a unified framework to iteratively and efficiently achieve high quality image restoration. Specifically, we introduce a restoration process that operates entirely in the latent domain to jointly perform degradation identification, image restoration, and quality verification. The resulting model is fully trainable end to end and allows for an all-in-one assess and restore approach that dynamically adapts the restoration process. Also, the tight integration of IQA and IR into a unified model minimizes the latency and information loss that typically arises from keeping the two modules disjoint, (e.g. during image and/or text decoding). Extensive experiments show that our approach consistent improvements under single, unknown and composite degradations, thereby establishing a new state-of-the-art.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Iterative Image Restoration
Image Quality Assessment
Latent Domain Processing
Unified Framework
Degradation Identification
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