Blind-Spot Guided Diffusion for Self-supervised Real-World Denoising
This provides a self-supervised solution for real-world image denoising, which is incremental as it builds on existing blind-spot and diffusion methods.
The paper tackles the problem of self-supervised real-world image denoising by addressing limitations in blind-spot networks and diffusion models, achieving state-of-the-art performance on SIDD and DND datasets.
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.