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.
@article{arxiv.2511.09965,
title = {Equivariant Sampling for Improving Diffusion Model-based Image Restoration},
author = {Chenxu Wu and Qingpeng Kong and Peiang Zhao and Wendi Yang and Wenxin Ma and Fenghe Tang and Zihang Jiang and S. Kevin Zhou},
journal= {arXiv preprint arXiv:2511.09965},
year = {2025}
}