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

• URL: http://arxiv.org/abs/2509.16091v1
• Date: Fri, 19 Sep 2025 15:35:07 GMT
• Title: Blind-Spot Guided Diffusion for Self-supervised Real-World Denoising
• Authors: Shen Cheng, Haipeng Li, Haibin Huang, Xiaohong Liu, Shuaicheng Liu,
• Abstract summary: Blind-Spot Guided Diffusion is a novel self-supervised framework for real-world image denoising.<n>Our approach addresses two major challenges: the limitations of blind-spot networks (BSNs) and the difficulty of adapting diffusion models to self-supervised denoising.
• Score: 55.099717395320276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Frequency Domain-Based Diffusion Model for Unpaired Image Dehazing [92.61216319417208]
  We propose a novel frequency domain-based diffusion model, named ours, for fully exploiting the beneficial knowledge in unpaired clear data.<n>Inspired by the strong generative ability shown by Diffusion Models (DMs), we tackle the dehazing task from the perspective of frequency domain reconstruction.
  arXiv  Detail & Related papers  (2025-07-02T01:22:46Z)
• Low-Trace Adaptation of Zero-shot Self-supervised Blind Image Denoising [23.758547513866766]
  We propose a trace-constraint loss function and low-trace adaptation Noise2Noise (LoTA-N2N) model to bridge the gap between self-supervised and supervised learning. Our method achieves state-of-the-art performance within the realm of zero-shot self-supervised image denoising approaches.
  arXiv  Detail & Related papers  (2024-03-19T02:47:33Z)
• Random Sub-Samples Generation for Self-Supervised Real Image Denoising [9.459398471988724]
  We propose a novel self-supervised real image denoising framework named Sampling Difference As Perturbation (SDAP) We find that adding an appropriate perturbation to the training images can effectively improve the performance of BSN. The results show that it significantly outperforms other state-of-the-art self-supervised denoising methods on real-world datasets.
  arXiv  Detail & Related papers  (2023-07-31T16:39:35Z)
• Self-supervised Image Denoising with Downsampled Invariance Loss and Conditional Blind-Spot Network [12.478287906337194]
  Most representative self-supervised denoisers are based on blind-spot networks. A standard blind-spot network fails to reduce real camera noise due to the pixel-wise correlation of noise. We propose a novel self-supervised training framework that can remove real noise.
  arXiv  Detail & Related papers  (2023-04-19T08:55:27Z)
• Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarios [44.31657750561106]
  Noise in real-world scenarios is often spatially correlated, which causes many self-supervised algorithms to perform poorly. We propose Asymmetric Tunable Blind-Spot Network (AT-BSN), where the blind-spot size can be freely adjusted. We show that our method achieves state-of-the-art, and is superior to other self-supervised algorithms in terms of computational overhead and visual effects.
  arXiv  Detail & Related papers  (2023-03-29T15:19:01Z)
• Robust Deep Ensemble Method for Real-world Image Denoising [62.099271330458066]
  We propose a simple yet effective Bayesian deep ensemble (BDE) method for real-world image denoising. Our BDE achieves +0.28dB PSNR gain over the state-of-the-art denoising method. Our BDE can be extended to other image restoration tasks, and achieves +0.30dB, +0.18dB and +0.12dB PSNR gains on benchmark datasets.
  arXiv  Detail & Related papers  (2022-06-08T06:19:30Z)
• AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network [60.650035708621786]
  Blind-spot network (BSN) and its variants have made significant advances in self-supervised denoising. It is challenging to deal with spatially correlated real-world noise using self-supervised BSN. Recently, pixel-shuffle downsampling (PD) has been proposed to remove the spatial correlation of real-world noise. We propose an Asymmetric PD (AP) to address this issue, which introduces different PD stride factors for training and inference.
  arXiv  Detail & Related papers  (2022-03-22T15:04:37Z)
• Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images [98.82804259905478]
  We present Neighbor2Neighbor to train an effective image denoising model with only noisy images. In detail, input and target used to train a network are images sub-sampled from the same noisy image. A denoising network is trained on sub-sampled training pairs generated in the first stage, with a proposed regularizer as additional loss for better performance.
  arXiv  Detail & Related papers  (2021-01-08T02:03:25Z)
• Learning Model-Blind Temporal Denoisers without Ground Truths [46.778450578529814]
  Denoisers trained with synthetic data often fail to cope with the diversity of unknown noises. Previous image-based method leads to noise overfitting if directly applied to video denoisers. We propose a general framework for video denoising networks that successfully addresses these challenges.
  arXiv  Detail & Related papers  (2020-07-07T07:19:48Z)
• Variational Denoising Network: Toward Blind Noise Modeling and Removal [59.36166491196973]
  Blind image denoising is an important yet very challenging problem in computer vision. We propose a new variational inference method, which integrates both noise estimation and image denoising.
  arXiv  Detail & Related papers  (2019-08-29T15:54:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.