A Nonlocal Bayesian Image Denoising Algorithm

By Marc Lebrun, A. Buades, Jean-Michel Morel. SIAM Journal of Imaging Sciences, 2013

325 citations recorded by the source corpus.

How researchers see this paper

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Image Blind Denoising with Generative Adversarial Network Based Noise Modeling (2018, methodology): “Multiscale [15] is an adaption of the Non-local Bayes approach [14]which assumes the noise model of each patch and its nearby patches to be zero-mean correlated Gaussian distributed.”
FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation (2019, methodology): “Methods such as these achieve performances comparable to those of well-known patch-based algorithms such as BM3D [10] or non-local Bayes (NLB [21]).”
Real Image Denoising With Feature Attention (2019, unknown): “Due to their success, many variants were proposed, including SADCT [27], SAPCA [20], NLB [37], and INLM [29] which seek self-similar patches in different transform domains.”
Seeing Motion in the Dark (2019, unknown): “State-of-the-art video denoising methods include VBM4D [32] and non-local Bayes [25], which rely on grouping similar patches and jointly filtering them to form the result.”
Toward Convolutional Blind Denoising of Real Photographs (2018, methodology): “[28, 29] propose an extension of non-local Bayes approach [27] by modeling the noise of each patch group to be zero-mean correlated Gaussian distributed.”