Universal Image Restoration via Internalized Chain-of-Thought Reasoning

• URL: http://arxiv.org/abs/2606.17557v1
• Date: Tue, 16 Jun 2026 06:01:36 GMT
• Title: Universal Image Restoration via Internalized Chain-of-Thought Reasoning
• Abstract summary: Image restoration seeks to recover high-quality images from degraded inputs but becomes highly ill-posed under complex, mixed degradations.<n>Recent works adopt Chain-of-Thought (CoT) reasoning for multi-round restoration using specialized modules.<n>We introduce CoTIR, a universal image restoration framework that internalizes CoT reasoning within a single model.
• Score: 52.763760468548504
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image restoration seeks to recover high-quality images from degraded inputs but becomes highly ill-posed under complex, mixed degradations. While unified all-in-one models are common, their performance declines as degradation complexity increases. Recent works adopt Chain-of-Thought (CoT) reasoning for multi-round restoration using specialized modules. However, this approach faces two key limitations: (i) increased computational cost due to multi-step processing, and (ii) weak modeling of interactions between degradations during stepwise inference. We introduce CoTIR, a universal image restoration framework that internalizes CoT reasoning within a single model. Concretely, we view image restoration as a specialized subtask of image editing, which implies that a large-scale pre-trained editing model provides a more favorable optimization starting point. Building on this, we fine-tune the model for restoration and further encode structured CoT-style reasoning into the learning objective via a differentiable formulation inspired by Lagrangian optimization, enabling holistic restoration without chaining specialized restorers. To facilitate training and evaluation, we further present CoTIR-Bench, a large-scale benchmark comprising 5.2 million samples with CoT-style reasoning traces. Extensive experiments on CoTIR-Bench and broad real composite degradation scenes show that CoTIR achieves stronger perceptual quality and more competitive fidelity than both all-in-one models and multi-round restoration methods. The source code is available at https://github.com/gy65896/CoTIR.

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