Restore, Assess, Repeat: A Unified Framework for Iterative Image Restoration

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.

Figures (17)

• Figure 1: Overview of RAR. Given an input image with unknown composite degradations, RAR operates entirely in latent by iteratively assessing current image quality, restoring the image based on the assessment, and verifying the quality improvement.
• Figure 2: To enable our RAR process, we define an LQA that fully integrates the input and out of the IQA into the latent space of the restoration module, using adapters $\mathcal{A}_I$ and $\mathcal{A}_Q$, respectively. Then, we instore a feedback loop between the two modules to iteratively recover best image quality. The dashed-line is only used for the very first iteration to feed $\mathbf{z}^0_{deg}$ to the restoration model.
• Figure 3: We illustrate our quality verification step. This is used at inference time to define a stopping criterion of the RAR process.
• Figure 4: Qualitative comparison on Composite Degradation.
• Figure 5: Qualitative analysis: Comparison with other multi-round methods (AutoDIR autodir, AgenticIR agenticir). Intermediate steps for our method are shown for illustrative purposes. First row: Composite degradations (haze + noise). RAR identifies and corrects them, also enhances contrast, making the result visually clearer. Second row: Single degradation (low light). AgenticIR fails due to inadequate tools for very dark scenes. Third row: Single degradation (haze). RAR successfully dehazes, but also improves low light, lowering the fidelity to ground truth. Fourth row: Unknown degradations. RAR demonstrates stronger generalization (also improving over ground truth).