Rethinking Deep Image Prior for Denoising

• URL: http://arxiv.org/abs/2108.12841v1
• Date: Sun, 29 Aug 2021 13:34:31 GMT
• Title: Rethinking Deep Image Prior for Denoising
• Authors: Yeonsik Jo, Se Young Chun and Jonghyun Choi
• Abstract summary: 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.
• Score: 23.140599133203292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep image prior (DIP) serves as a good inductive bias for diverse inverse problems. Among them, denoising is known to be particularly challenging for the DIP due to noise fitting with the requirement of an early stopping. To address the issue, we first analyze the DIP by the notion of effective degrees of freedom (DF) to monitor the optimization progress and propose a principled stopping criterion before fitting to noise without access of a paired ground truth image for Gaussian noise. We also propose the `stochastic temporal ensemble (STE)' method for incorporating techniques to further improve DIP's performance for denoising. We additionally extend our method to Poisson noise. Our empirical validations show that given a single noisy image, our method denoises the image while preserving rich textual details. Further, our approach outperforms prior arts in LPIPS by large margins with comparable PSNR and SSIM on seven different datasets.

Related papers

• How to Best Combine Demosaicing and Denoising? [16.921538543268216]
  demosaicing and denoising play a critical role in the raw imaging pipeline. Most demosaicing methods address the demosaicing of noise free images. The real problem is to jointly denoise and demosaic noisy raw images.
  arXiv  Detail & Related papers  (2024-08-13T07:23:53Z)
• 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)
• Back to Basics: Fast Denoising Iterative Algorithm [0.0]
  We introduce Back to Basics (BTB), a fast iterative algorithm for noise reduction. We examine three study cases: natural image denoising in the presence of additive white Gaussian noise, Poisson-distributed image denoising, and speckle suppression in optical coherence tomography ( OCT) Experimental results demonstrate that the proposed approach can effectively improve image quality, in challenging noise settings.
  arXiv  Detail & Related papers  (2023-11-11T18:32:06Z)
• Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoising [14.486289176696438]
  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.
  arXiv  Detail & Related papers  (2023-08-09T03:26:58Z)
• 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)
• Representing Noisy Image Without Denoising [91.73819173191076]
  Fractional-order Moments in Radon space (FMR) is designed to derive robust representation directly from noisy images. Unlike earlier integer-order methods, our work is a more generic design taking such classical methods as special cases.
  arXiv  Detail & Related papers  (2023-01-18T10:13:29Z)
• Poisson2Sparse: Self-Supervised Poisson Denoising From a Single Image [34.27748767631027]
  We present a novel self-supervised learning method for single-image denoising. We approximate traditional iterative optimization algorithms for image denoising with a recurrent neural network. Our method outperforms the state-of-the-art approaches in terms of PSNR and SSIM.
  arXiv  Detail & Related papers  (2022-06-04T00:08:58Z)
• Zero-shot Blind Image Denoising via Implicit Neural Representations [77.79032012459243]
  We propose an alternative denoising strategy that leverages the architectural inductive bias of implicit neural representations (INRs) We show that our method outperforms existing zero-shot denoising methods under an extensive set of low-noise or real-noise scenarios.
  arXiv  Detail & Related papers  (2022-04-05T12:46:36Z)
• On Measuring and Controlling the Spectral Bias of the Deep Image Prior [63.88575598930554]
  The deep image prior has demonstrated the remarkable ability that untrained networks can address inverse imaging problems. It requires an oracle to determine when to stop the optimization as the performance degrades after reaching a peak. We study the deep image prior from a spectral bias perspective to address these problems.
  arXiv  Detail & Related papers  (2021-07-02T15:10:42Z)
• 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.