Equivariant Sampling for Improving Diffusion Model-based Image Restoration
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912706451210240 |
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| author | Wu, Chenxu Kong, Qingpeng Zhao, Peiang Yang, Wendi Ma, Wenxin Tang, Fenghe Jiang, Zihang Zhou, S. Kevin |
| author_facet | Wu, Chenxu Kong, Qingpeng Zhao, Peiang Yang, Wendi Ma, Wenxin Tang, Fenghe Jiang, Zihang Zhou, S. Kevin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_09965 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Equivariant Sampling for Improving Diffusion Model-based Image Restoration Wu, Chenxu Kong, Qingpeng Zhao, Peiang Yang, Wendi Ma, Wenxin Tang, Fenghe Jiang, Zihang Zhou, S. Kevin Computer Vision and Pattern Recognition 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. |
| title | Equivariant Sampling for Improving Diffusion Model-based Image Restoration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.09965 |