Blind-Spot Guided Diffusion for Self-supervised Real-World Denoising
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908548023189504 |
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| author | Cheng, Shen Li, Haipeng Huang, Haibin Liu, Xiaohong Liu, Shuaicheng |
| author_facet | Cheng, Shen Li, Haipeng Huang, Haibin Liu, Xiaohong Liu, Shuaicheng |
| contents | In this work, we present Blind-Spot Guided Diffusion, a novel self-supervised framework for real-world image denoising. Our approach addresses two major challenges: the limitations of blind-spot networks (BSNs), which often sacrifice local detail and introduce pixel discontinuities due to spatial independence assumptions, and the difficulty of adapting diffusion models to self-supervised denoising. We propose a dual-branch diffusion framework that combines a BSN-based diffusion branch, generating semi-clean images, with a conventional diffusion branch that captures underlying noise distributions. To enable effective training without paired data, we use the BSN-based branch to guide the sampling process, capturing noise structure while preserving local details. Extensive experiments on the SIDD and DND datasets demonstrate state-of-the-art performance, establishing our method as a highly effective self-supervised solution for real-world denoising. Code and pre-trained models are released at: https://github.com/Sumching/BSGD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16091 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Blind-Spot Guided Diffusion for Self-supervised Real-World Denoising Cheng, Shen Li, Haipeng Huang, Haibin Liu, Xiaohong Liu, Shuaicheng Computer Vision and Pattern Recognition In this work, we present Blind-Spot Guided Diffusion, a novel self-supervised framework for real-world image denoising. Our approach addresses two major challenges: the limitations of blind-spot networks (BSNs), which often sacrifice local detail and introduce pixel discontinuities due to spatial independence assumptions, and the difficulty of adapting diffusion models to self-supervised denoising. We propose a dual-branch diffusion framework that combines a BSN-based diffusion branch, generating semi-clean images, with a conventional diffusion branch that captures underlying noise distributions. To enable effective training without paired data, we use the BSN-based branch to guide the sampling process, capturing noise structure while preserving local details. Extensive experiments on the SIDD and DND datasets demonstrate state-of-the-art performance, establishing our method as a highly effective self-supervised solution for real-world denoising. Code and pre-trained models are released at: https://github.com/Sumching/BSGD. |
| title | Blind-Spot Guided Diffusion for Self-supervised Real-World Denoising |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.16091 |