Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoising

• URL: http://arxiv.org/abs/2308.04682v1
• Date: Wed, 9 Aug 2023 03:26:58 GMT
• Title: Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoising
• Authors: Jun Cheng, Tao Liu, Shan Tan
• Abstract summary: We propose a score priors-guided deep variational inference, namely ScoreDVI, for practical real-world denoising. We exploit a Non-$i.i.d$ Gaussian mixture model and variational noise posterior to model the real-world noise. Our method outperforms other single image-based real-world denoising methods and achieves comparable performance to dataset-based unsupervised methods.
• Score: 14.486289176696438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world single image denoising is crucial and practical in computer vision. Bayesian inversions combined with score priors now have proven effective for single image denoising but are limited to white Gaussian noise. Moreover, applying existing score-based methods for real-world denoising requires not only the explicit train of score priors on the target domain but also the careful design of sampling procedures for posterior inference, which is complicated and impractical. To address these limitations, we propose a score priors-guided deep variational inference, namely ScoreDVI, for practical real-world denoising. By considering the deep variational image posterior with a Gaussian form, score priors are extracted based on easily accessible minimum MSE Non-$i.i.d$ Gaussian denoisers and variational samples, which in turn facilitate optimizing the variational image posterior. Such a procedure adaptively applies cheap score priors to denoising. Additionally, we exploit a Non-$i.i.d$ Gaussian mixture model and variational noise posterior to model the real-world noise. This scheme also enables the pixel-wise fusion of multiple image priors and variational image posteriors. Besides, we develop a noise-aware prior assignment strategy that dynamically adjusts the weight of image priors in the optimization. Our method outperforms other single image-based real-world denoising methods and achieves comparable performance to dataset-based unsupervised methods.

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)
• Beyond Image Prior: Embedding Noise Prior into Conditional Denoising Transformer [17.430622649002427]
  Existing learning-based denoising methods typically train models to generalize the image prior from large-scale datasets. We propose a new perspective on the denoising challenge by highlighting the distinct separation between noise and image priors. We introduce a Locally Noise Prior Estimation algorithm, which accurately estimates the noise prior directly from a single raw noisy image.
  arXiv  Detail & Related papers  (2024-07-12T08:43:11Z)
• Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-Regulation [55.07472635587852]
  Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast. These approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios. We first propose a method for estimating the noise level in low light images in a quick and accurate way. We then devise a Learnable Illumination Interpolator (LII) to satisfy general constraints between illumination and input.
  arXiv  Detail & Related papers  (2023-05-17T13:56:48Z)
• Masked Image Training for Generalizable Deep Image Denoising [53.03126421917465]
  We present a novel approach to enhance the generalization performance of denoising networks. Our method involves masking random pixels of the input image and reconstructing the missing information during training. Our approach exhibits better generalization ability than other deep learning models and is directly applicable to real-world scenarios.
  arXiv  Detail & Related papers  (2023-03-23T09:33:44Z)
• Linear Combinations of Patches are Unreasonably Effective for Single-Image Denoising [5.893124686141782]
  Deep neural networks have revolutionized image denoising in achieving significant accuracy improvements. To alleviate the requirement to learn image priors externally, single-image methods perform denoising solely based on the analysis of the input noisy image. This work investigates the effectiveness of linear combinations of patches for denoising under this constraint.
  arXiv  Detail & Related papers  (2022-12-01T10:52:03Z)
• Enhancing convolutional neural network generalizability via low-rank weight approximation [6.763245393373041]
  Sufficient denoising is often an important first step for image processing. Deep neural networks (DNNs) have been widely used for image denoising. We introduce a new self-supervised framework for image denoising based on the Tucker low-rank tensor approximation.
  arXiv  Detail & Related papers  (2022-09-26T14:11:05Z)
• 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)
• Rethinking Deep Image Prior for Denoising [23.140599133203292]
  We analyze the DIP by the notion of effective degrees of freedom (DF) to monitor the optimization progress. We propose a principled stopping criterion before fitting to noise without access of a paired ground truth image for Gaussian noise. Our approach outperforms prior arts in LPIPS by large margins with comparable PSNR and SSIM on seven different datasets.
  arXiv  Detail & Related papers  (2021-08-29T13:34:31Z)
• 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)
• Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders [81.30960319178725]
  We propose DivNoising, a denoising approach based on fully convolutional variational autoencoders (VAEs) First we introduce a principled way of formulating the unsupervised denoising problem within the VAE framework by explicitly incorporating imaging noise models into the decoder. We show that such a noise model can either be measured, bootstrapped from noisy data, or co-learned during training.
  arXiv  Detail & Related papers  (2020-06-10T21:28:13Z)
• 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.