Blind-Spot Guided Diffusion for Self-supervised Real-World Denoising

Computer Vision & MultiModal AI

Published: arXiv: 2509.16091v1

Authors

Shen Cheng Haipeng Li Haibin Huang Xiaohong Liu Shuaicheng Liu

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.

Paper Summary

Problem

Image denoising is a fundamental task in computer vision that involves recovering a clean image from a noisy observation. This is a challenging problem because both the image and noise components are unknown and difficult to disentangle. Traditional methods require paired noisy-clean images for training, but acquiring such data at scale is resource-intensive and often impractical. Self-supervised learning methods have emerged as a promising alternative, but they often struggle to preserve local details and introduce pixel discontinuities.

Key Innovation

The researchers propose a novel self-supervised framework called Blind-Spot Guided Diffusion (BSGD) that addresses the limitations of blind-spot networks (BSNs) and adapts diffusion models to self-supervised denoising. BSGD is a dual-branch diffusion framework that combines a BSN-based diffusion branch and a conventional diffusion branch. The BSN-based branch generates semi-clean images, while the conventional branch captures underlying noise distributions. The BSN-based branch is used to guide the sampling process, capturing noise structure while preserving local details.

Practical Impact

BSGD has the potential to revolutionize image denoising for real-world applications. By leveraging self-supervised learning, BSGD can be trained on unpaired noisy images, making it a more practical and efficient solution. The framework's ability to preserve local details and avoid pixel discontinuities makes it a highly effective solution for denoising real-world images. This can have significant implications for various applications, such as medical imaging, surveillance, and photography.

Analogy / Intuitive Explanation

Imagine you're trying to clean a dirty window. Traditional methods might use a single cleaning solution that works well on some parts of the window but not others. BSGD is like using a combination of cleaning solutions, one that focuses on removing dirt and grime (the BSN-based branch) and another that captures the underlying texture and pattern of the window (the conventional branch). By combining these two solutions, BSGD can effectively clean the window while preserving its original texture and pattern.

Paper Information

Categories:

cs.CV

Published Date:

arXiv ID:

2509.16091v1

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