Self-supervised Dynamic Heterogeneous Degradation Modeling for Unified Zero-Shot Image Restoration

• URL: http://arxiv.org/abs/2605.24593v1
• Date: Sat, 23 May 2026 14:08:23 GMT
• Title: Self-supervised Dynamic Heterogeneous Degradation Modeling for Unified Zero-Shot Image Restoration
• Abstract summary: Zero-shot image restoration provides a flexible way to handle diverse degradations without task-specific training.<n>We propose a unified physical zero-shot image restoration framework that explicitly models heterogeneous degradations into a homogeneous all-in-one distribution.<n>Our method achieves state-of-the-art performance across both single and mixed degradations.
• Score: 36.68260793473452
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Zero-shot image restoration provides a flexible way to handle diverse degradations without task-specific training. However, existing methods typically rely on stacked layers or pre-trained features to enhance degradation expression, while overlooking physically consistent priors. The insufficient degradation prompts impose the heavy training burden and high sampling costs during zero-shot diffusion. Moreover, the fixed inference trajectory often collapses to suboptimal solutions under complex corruptions. We observe that heterogeneous degradations can be reparameterized into a minimal set of physically coherent parameters for compact representation. Based on this insight, we first propose a unified physical zero-shot image restoration (UP-ZeroIR) framework that explicitly models heterogeneous degradations into a homogeneous all-in-one distribution. The distribution can be optimized directly in the latent space, enabling principled solution exploration and effective prompt adaptation. Besides, we introduce a dynamic quality-refinement strategy that adaptively adjusts the diffusion trajectory for robust globally optimal convergence. Extensive experiments demonstrate that our method achieves state-of-the-art performance across both single and mixed degradations. Our code is available at https://github.com/yangjinglyy/UP-ZeroIR

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