{"ID":2868907,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16091","arxiv_id":"2509.16091","title":"Blind-Spot Guided Diffusion for Self-supervised Real-World Denoising","abstract":"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.","short_abstract":"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 assumptio...","url_abs":"https://arxiv.org/abs/2509.16091","url_pdf":"https://arxiv.org/pdf/2509.16091v1","authors":"[\"Shen Cheng\",\"Haipeng Li\",\"Haibin Huang\",\"Xiaohong Liu\",\"Shuaicheng Liu\"]","published":"2025-09-19T15:35:07Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false,"code_links":[{"ID":609632,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2868907,"paper_url":"https://arxiv.org/abs/2509.16091","paper_title":"Blind-Spot Guided Diffusion for Self-supervised Real-World Denoising","repo_url":"https://github.com/Sumching/BSGD","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}