A Comparison of Image Denoising Methods

• URL: http://arxiv.org/abs/2304.08990v2
• Date: Tue, 9 May 2023 05:57:21 GMT
• Title: A Comparison of Image Denoising Methods
• Authors: Zhaoming Kong, Fangxi Deng, Haomin Zhuang, Jun Yu, Lifang He and Xiaowei Yang
• Abstract summary: We compare a variety of denoising methods on both synthetic and real-world datasets for different applications. We show that a simple matrix-based algorithm may be able to produce similar results compared with its tensor counterparts. In spite of the progress in recent years, we discuss shortcomings and possible extensions of existing techniques.
• Score: 23.69991964391047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The advancement of imaging devices and countless images generated everyday pose an increasingly high demand on image denoising, which still remains a challenging task in terms of both effectiveness and efficiency. To improve denoising quality, numerous denoising techniques and approaches have been proposed in the past decades, including different transforms, regularization terms, algebraic representations and especially advanced deep neural network (DNN) architectures. Despite their sophistication, many methods may fail to achieve desirable results for simultaneous noise removal and fine detail preservation. In this paper, to investigate the applicability of existing denoising techniques, we compare a variety of denoising methods on both synthetic and real-world datasets for different applications. We also introduce a new dataset for benchmarking, and the evaluations are performed from four different perspectives including quantitative metrics, visual effects, human ratings and computational cost. Our experiments demonstrate: (i) the effectiveness and efficiency of representative traditional denoisers for various denoising tasks, (ii) a simple matrix-based algorithm may be able to produce similar results compared with its tensor counterparts, and (iii) the notable achievements of DNN models, which exhibit impressive generalization ability and show state-of-the-art performance on various datasets. In spite of the progress in recent years, we discuss shortcomings and possible extensions of existing techniques. Datasets, code and results are made publicly available and will be continuously updated at https://github.com/ZhaomingKong/Denoising-Comparison.

Related papers

• 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)
• 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)
• Deep Semantic Statistics Matching (D2SM) Denoising Network [70.01091467628068]
  We introduce the Deep Semantic Statistics Matching (D2SM) Denoising Network. It exploits semantic features of pretrained classification networks, then it implicitly matches the probabilistic distribution of clear images at the semantic feature space. By learning to preserve the semantic distribution of denoised images, we empirically find our method significantly improves the denoising capabilities of networks.
  arXiv  Detail & Related papers  (2022-07-19T14:35:42Z)
• 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)
• Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging [0.0]
  We propose Deformed2Self, an end-to-end self-supervised deep learning framework for dynamic imaging denoising. It combines single-image and multi-image denoising to improve image quality and use a spatial transformer network to model motion between different slices.
  arXiv  Detail & Related papers  (2021-06-23T05:50:19Z)
• Image Denoising using Attention-Residual Convolutional Neural Networks [0.0]
  We propose a new learning-based non-blind denoising technique named Attention Residual Convolutional Neural Network (ARCNN) and its extension to blind denoising named Flexible Attention Residual Convolutional Neural Network (FARCNN) ARCNN achieved an overall average PSNR results of around 0.44dB and 0.96dB for Gaussian and Poisson denoising, respectively FARCNN presented very consistent results, even with slightly worsen performance compared to ARCNN.
  arXiv  Detail & Related papers  (2021-01-19T16:37:57Z)
• A Comprehensive Comparison of Multi-Dimensional Image Denoising Methods [14.702885691557638]
  We extensively compare over 60 methods on both synthetic and real-world datasets. We show that a simple matrix-based algorithm could produce similar results compared with its tensor counterparts. Several models trained with synthetic Gaussian noise show state-of-the-art performance on real-world color image and video datasets.
  arXiv  Detail & Related papers  (2020-11-06T16:28:17Z)
• Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising [54.730707387866076]
  We introduce Noise2Same, a novel self-supervised denoising framework. In particular, Noise2Same requires neither J-invariance nor extra information about the noise model. Our results show that our Noise2Same remarkably outperforms previous self-supervised denoising methods.
  arXiv  Detail & Related papers  (2020-10-22T18:12:26Z)
• 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)
• Noise2Inverse: Self-supervised deep convolutional denoising for tomography [0.0]
  Noise2Inverse is a deep CNN-based denoising method for linear image reconstruction algorithms. We develop a theoretical framework which shows that such training indeed obtains a denoising CNN. On simulated CT datasets, Noise2Inverse demonstrates an improvement in peak signal-to-noise ratio and structural similarity index.
  arXiv  Detail & Related papers  (2020-01-31T12:50:24Z)
• 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.