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arXiv · 2511.09965

Equivariant Sampling for Improving Diffusion Model-based Image Restoration

Chenxu Wu·Qingpeng Kong·Peiang Zhao·Wendi Yang·Wenxin Ma·Fenghe Tang·Zihang Jiang·S. Kevin Zhou
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Abstract

Recent advances in generative models, especially diffusion models, have significantly improved image restoration (IR) performance. However, existing problem-agnostic diffusion model-based image restoration (DMIR) methods face challenges in fully leveraging diffusion priors, resulting in suboptimal performance. In this paper, we address the limitations of current problem-agnostic DMIR methods by analyzing their sampling process and providing effective solutions. We introduce EquS, a DMIR method that imposes equivariant information through dual sampling trajectories. To further boost EquS, we propose the Timestep-Aware Schedule (TAS) and introduce EquS$^+$. TAS prioritizes deterministic steps to enhance certainty and sampling efficiency. Extensive experiments on benchmarks demonstrate that our method is compatible with previous problem-agnostic DMIR methods and significantly boosts their performance without increasing computational costs. Our code is available at https://github.com/FouierL/EquS.

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Chenxu Wu, Qingpeng Kong, Peiang Zhao, Wendi Yang, Wenxin Ma, Fenghe Tang, Zihang Jiang, S. Kevin Zhou. 2025-11-13. Equivariant Sampling for Improving Diffusion Model-based Image Restoration. https://arxiv.org/abs/2511.09965

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