A Nonlocal Bayesian Image Denoising Algorithm

Lebrun, Marc; Buades, Antoni; Morel, Jean‐Michel

doi:10.1137/120874989

Cited by 295 publications

(286 citation statements)

References 23 publications

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“…Besides the obvious use of these techniques on a frame by frame basis, local average methods, such as the bilateral filter [19], or patch based methods such as NL-means [4] or BM3D [7] and NLBayes [12] can be easily adapted to video just by extending the neighboring area to the adjacent frames. Kervrann and Boulanger [3] extended NL-means to video by growing adaptively the spatio-temporal neighborhood.…”

Section: Introductionmentioning

confidence: 99%

“…Kervrann and Boulanger [3] extended NL-means to video by growing adaptively the spatio-temporal neighborhood. Arias et al extended NL-Bayes [12] to video [1,2]. The BM3D extension, VBM4D [14], exploits the mutual similarity between 3-D spatio-temporal volumes constructed by tracking blocks along trajectories defined by the motion vectors.…”

Section: Introductionmentioning

confidence: 99%

“…For large values of noise standard deviation some residual noise might be left in flat zones. This is a common issue to image denoising methods using an adaptive basis [12]. The reason is that in flat zones, the selection of patches is taking into account only noise because of the lack of geometry in these parts, which includes a bias in the selection process.…”

mentioning

confidence: 99%

“…In addition, the PCA analysis with pure noise patches and a reduced number of samples might generate principal values larger than expected. We apply the same solution to this issue as proposed in [12]. Whenever a set of patches is detected to be flat up to the noise oscillations, a simple average (per color channel) of all values in the patches is taken instead of computing a PCA model (see Algorithm 8).…”

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confidence: 99%

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Video Denoising with Optical Flow Estimation

Buades¹,

Lisani²

2018

Image Processing on Line

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In this paper we describe the implementation of state-of-the-art video denoising algorithm SPTWO [A. Buades, J.L. Lisani, M. Miladinović, Patch Based Video Denoising with Optical Flow Estimation, IEEE Transactions on Image Processing 25 (6), [2573][2574][2575][2576][2577][2578][2579][2580][2581][2582][2583][2584][2585][2586]. This algorithm, inspired by image fusion techniques, uses motion compensation by regularized optical flow methods, which permits robust patch comparison in spatiotemporal volumes. Groups of similar patches are denoised using Principal Component Analysis, which ensures the correct preservation of fine texture and details. Source CodeThe reviewed source code and documentation for this algorithm are available from the web page of this article 1 .

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Video Denoising with Optical Flow Estimation

Buades¹,

Lisani²

2018

Image Processing on Line

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“…Among these methods, one of the most popular remains the Non-Local Means [5], which sees similar patches as independent realizations of the same distribution and averages these repeated structures to reduce noise variance. If numerous approaches have built on the same core ideas since 2004, the recent and most convincing approaches in patch-based denoising rely on a Bayesian reformulation of the denoising problem, using local or global statistical priors for the distribution of each patch [12,24,23,20,1,11]. Under the white Gaussian noise model (2), the conditional distribution of a noisy patch y knowing its original version x (we omit the index i in the following) can be written…”

Section: Introductionmentioning

confidence: 99%

Gaussian Priors for Image Denoising

Delon

Houdard

2018

Denoising of Photographic Images and Video

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This chapter is dedicated to the study of Gaussian priors for patch-based image denoising. In the last twelve years, patch priors have been widely used for image restoration. In a Bayesian framework, such priors on patches can be used for instance to estimate a clean patch from its noisy version, via classical estimators such as the conditional expectation or the maximum a posteriori. As we will recall, in the case of Gaussian white noise, simply assuming Gaussian (or Mixture of Gaussians) priors on patches leads to very simple closed-form expressions for some of these estimators. Nevertheless, the convenience of such models should not prevail over their relevance. For this reason, we also discuss how these models represent patches and what kind of information they encode. The end of the chapter focuses on the different ways in which these models can be learned on real data. This stage is particularly challenging because of the curse of dimensionality. Through these different questions, we compare and connect several denoising methods using this framework.

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Image Denoising Algorithms: From Wavelet Shrinkage to Nonlocal Collaborative Filtering

Pižurica¹

2017

Wiley Encyclopedia of Electrical and Electronics Engineering

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This article presents an overview of image denoising algorithms ranging from wavelet shrinkage to patch‐based nonlocal processing. The focus is on the suppression of additive Gaussian noise (white and colored). A great attention is devoted to explaining the main underlying ideas and concepts of representative approaches, complemented by illustrative examples, accessible also to nonexperts in the field. A Bayesian perspective of wavelet shrinkage is given, with different instances of spatial context modeling (including local spatial activity indicators, Markov random fields, hidden Markov tree models, and Gaussian scale mixture models). Extensions to other transform domains (curvelets and other generalizations of wavelets) are addressed too, showing the benefits in terms of image quality. Patch‐based image denoising is illustrated with principles of nonlocal means filtering and collaborative filtering, explaining also the connections with dictionary learning. Some general notes on the performance comparison are given, by summarizing the benefits and limitations of various approaches against each other, and pointing to some of the current trends in the field.

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A Nonlocal Bayesian Image Denoising Algorithm

Cited by 295 publications

References 23 publications

Video Denoising with Optical Flow Estimation

Video Denoising with Optical Flow Estimation

Gaussian Priors for Image Denoising

Image Denoising Algorithms: From Wavelet Shrinkage to Nonlocal Collaborative Filtering

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