Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling

• URL: http://arxiv.org/abs/2307.03992v5
• Date: Sun, 17 Nov 2024 14:27:58 GMT
• Title: Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling
• Authors: Tong Li, Hansen Feng, Lizhi Wang, Zhiwei Xiong, Hua Huang,
• Abstract summary: 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.
• Score: 56.506240377714754
• License:
• Abstract: Image denoising is a fundamental problem in computational photography, where achieving high perception with low distortion is highly demanding. Current methods either struggle with perceptual quality or suffer from significant distortion. Recently, the emerging diffusion model has achieved state-of-the-art performance in various tasks and demonstrates great potential for image denoising. However, stimulating diffusion models for image denoising is not straightforward and requires solving several critical problems. For one thing, the input inconsistency hinders the connection between diffusion models and image denoising. For another, the content inconsistency between the generated image and the desired denoised image introduces distortion. To tackle these problems, we present a novel strategy called the Diffusion Model for Image Denoising (DMID) by understanding and rethinking the diffusion model from a denoising perspective. Our DMID strategy includes an adaptive embedding method that embeds the noisy image into a pre-trained unconditional diffusion model and an adaptive ensembling method that reduces distortion in the denoised image. Our DMID strategy achieves state-of-the-art performance on both distortion-based and perception-based metrics, for both Gaussian and real-world image denoising.The code is available at https://github.com/Li-Tong-621/DMID.

Related papers

• NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation [86.7260950382448]
  We propose a novel approach to correct noise for image validity, NoiseDiffusion. NoiseDiffusion performs within the noisy image space and injects raw images into these noisy counterparts to address the challenge of information loss.
  arXiv  Detail & Related papers  (2024-03-13T12:32:25Z)
• Gradpaint: Gradient-Guided Inpainting with Diffusion Models [71.47496445507862]
  Denoising Diffusion Probabilistic Models (DDPMs) have recently achieved remarkable results in conditional and unconditional image generation. We present GradPaint, which steers the generation towards a globally coherent image. We generalizes well to diffusion models trained on various datasets, improving upon current state-of-the-art supervised and unsupervised methods.
  arXiv  Detail & Related papers  (2023-09-18T09:36:24Z)
• ExposureDiffusion: Learning to Expose for Low-light Image Enhancement [87.08496758469835]
  This work addresses the issue by seamlessly integrating a diffusion model with a physics-based exposure model. Our method obtains significantly improved performance and reduced inference time compared with vanilla diffusion models. The proposed framework can work with both real-paired datasets, SOTA noise models, and different backbone networks.
  arXiv  Detail & Related papers  (2023-07-15T04:48:35Z)
• 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)
• Denoising Diffusion Models for Plug-and-Play Image Restoration [135.6359475784627]
  This paper proposes DiffPIR, which integrates the traditional plug-and-play method into the diffusion sampling framework. Compared to plug-and-play IR methods that rely on discriminative Gaussian denoisers, DiffPIR is expected to inherit the generative ability of diffusion models.
  arXiv  Detail & Related papers  (2023-05-15T20:24:38Z)
• 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)
• Diffusion Model for Generative Image Denoising [17.897180118637856]
  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.
  arXiv  Detail & Related papers  (2023-02-05T14:53:07Z)
• EDICT: Exact Diffusion Inversion via Coupled Transformations [13.996171129586731]
  Finding an initial noise vector that produces an input image when fed into the diffusion process (known as inversion) is an important problem. We propose Exact Diffusion Inversion via Coupled Transformations (EDICT), an inversion method that draws inspiration from affine coupling layers. EDICT enables mathematically exact inversion of real and model-generated images by maintaining two coupled noise vectors.
  arXiv  Detail & Related papers  (2022-11-22T18:02:49Z)
• 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.