Equivariant Sampling for Improving Diffusion Model-based Image Restoration

• URL: http://arxiv.org/abs/2511.09965v1
• Date: Fri, 14 Nov 2025 01:22:30 GMT
• Title: Equivariant Sampling for Improving Diffusion Model-based Image Restoration
• Authors: Chenxu Wu, Qingpeng Kong, Peiang Zhao, Wendi Yang, Wenxin Ma, Fenghe Tang, Zihang Jiang, S. Kevin Zhou,
• Abstract summary: We introduce EquS, a DMIR method that imposes equivariant information through dual sampling trajectories.<n>To further boost EquS, we propose the Timestep-Aware Schedule (TAS) and introduce EquS$+$.<n>Our method is compatible with previous problem-agnostic DMIR methods and significantly boosts their performance without increasing computational costs.
• Score: 25.06154860408637
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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