Diffusion Model for Generative Image Denoising

• URL: http://arxiv.org/abs/2302.02398v1
• Date: Sun, 5 Feb 2023 14:53:07 GMT
• Title: Diffusion Model for Generative Image Denoising
• Authors: Yutong Xie, Minne Yuan, Bin Dong and Quanzheng Li
• Abstract summary: In supervised learning for image denoising, usually the paired clean images and noisy images are collected and synthesised to train a denoising model. In this paper, we regard the denoising task as a problem of estimating the posterior distribution of clean images conditioned on noisy images.
• Score: 17.897180118637856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In supervised learning for image denoising, usually the paired clean images and noisy images are collected or synthesised to train a denoising model. L2 norm loss or other distance functions are used as the objective function for training. It often leads to an over-smooth result with less image details. In this paper, we regard the denoising task as a problem of estimating the posterior distribution of clean images conditioned on noisy images. We apply the idea of diffusion model to realize generative image denoising. According to the noise model in denoising tasks, we redefine the diffusion process such that it is different from the original one. Hence, the sampling of the posterior distribution is a reverse process of dozens of steps from the noisy image. We consider three types of noise model, Gaussian, Gamma and Poisson noise. With the guarantee of theory, we derive a unified strategy for model training. Our method is verified through experiments on three types of noise models and achieves excellent performance.

Related papers

• Self-Calibrated Variance-Stabilizing Transformations for Real-World Image Denoising [19.08732222562782]
  Supervised deep learning has become the method of choice for image denoising. We show that, contrary to popular belief, denoising networks specialized in the removal of Gaussian noise can be efficiently leveraged in favor of real-world image denoising.
  arXiv  Detail & Related papers  (2024-07-24T16:23:46Z)
• Blue noise for diffusion models [50.99852321110366]
  We introduce a novel and general class of diffusion models taking correlated noise within and across images into account. Our framework allows introducing correlation across images within a single mini-batch to improve gradient flow. We perform both qualitative and quantitative evaluations on a variety of datasets using our method.
  arXiv  Detail & Related papers  (2024-02-07T14:59:25Z)
• Stimulating the Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling [56.506240377714754]
  We present a novel strategy called the Diffusion Model for Image Denoising (DMID) Our strategy includes an adaptive embedding method that embeds the noisy image into a pre-trained unconditional diffusion model. Our DMID strategy achieves state-of-the-art performance on both distortion-based and perception-based metrics.
  arXiv  Detail & Related papers  (2023-07-08T14:59:41Z)
• SVNR: Spatially-variant Noise Removal with Denoising Diffusion [43.2405873681083]
  We present a novel formulation of denoising diffusion that assumes a more realistic, spatially-variant noise model. In experiments we demonstrate the advantages of our approach over a strong diffusion model baseline, as well as over a state-of-the-art single image denoising method.
  arXiv  Detail & Related papers  (2023-06-28T09:32:00Z)
• Real-World Denoising via Diffusion Model [14.722529440511446]
  Real-world image denoising aims to recover clean images from noisy images captured in natural environments. diffusion models have achieved very promising results in the field of image generation, outperforming previous generation models. This paper proposes a novel general denoising diffusion model that can be used for real-world image denoising.
  arXiv  Detail & Related papers  (2023-05-08T04:48:03Z)
• Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images [35.29066692454865]
  This paper proposes a framework for training a noise model and a denoiser simultaneously. It relies on pairs of noisy images rather than noisy/clean paired image data. The trained denoiser is shown to significantly improve upon both supervised and weakly supervised baseline denoising approaches.
  arXiv  Detail & Related papers  (2022-06-02T15:31:40Z)
• Noise Distribution Adaptive Self-Supervised Image Denoising using Tweedie Distribution and Score Matching [29.97769511276935]
  We show that Tweedie distributions play key roles in modern deep learning era, leading to a distribution independent self-supervised image denoising formula without clean reference images. Specifically, by combining with the recent Noise2Score self-supervised image denoising approach and the saddle point approximation of Tweedie distribution, we can provide a general closed-form denoising formula. We show that the proposed method can accurately estimate noise models and parameters, and provide the state-of-the-art self-supervised image denoising performance in the benchmark dataset and real-world dataset.
  arXiv  Detail & Related papers  (2021-12-05T04:36:08Z)
• IDR: Self-Supervised Image Denoising via Iterative Data Refinement [66.5510583957863]
  We present a practical unsupervised image denoising method to achieve state-of-the-art denoising performance. Our method only requires single noisy images and a noise model, which is easily accessible in practical raw image denoising. To evaluate raw image denoising performance in real-world applications, we build a high-quality raw image dataset SenseNoise-500 that contains 500 real-life scenes.
  arXiv  Detail & Related papers  (2021-11-29T07:22:53Z)
• 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)
• Unpaired Learning of Deep Image Denoising [80.34135728841382]
  This paper presents a two-stage scheme by incorporating self-supervised learning and knowledge distillation. For self-supervised learning, we suggest a dilated blind-spot network (D-BSN) to learn denoising solely from real noisy images. Experiments show that our unpaired learning method performs favorably on both synthetic noisy images and real-world noisy photographs.
  arXiv  Detail & Related papers  (2020-08-31T16:22:40Z)
• Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation [52.75909685172843]
  Real-world image noise removal is a long-standing yet very challenging task in computer vision. We propose a novel unified framework to deal with the noise removal and noise generation tasks. Our method learns the joint distribution of the clean-noisy image pairs.
  arXiv  Detail & Related papers  (2020-07-12T09:16:06Z)

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.