About: Image denoising with less artefacts: Novel non-linear filtering on fast patch reorderings

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About: Image denoising with less artefacts: Novel non-linear filtering on fast patch reorderings Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Image denoising with less artefacts: Novel non-linear filtering on fast patch reorderings
Creator	Bodduna, Kireeti Weickert, Joachim
source	ArXiv
abstract	Leading denoising methods such as 3D block matching (BM3D) are patch-based. However, they can suffer from frequency domain artefacts and require to specify explicit noise models. We present a patch-based method that avoids these drawbacks. It combines a simple and fast patch reordering with a non-linear smoothing. The smoothing rewards both patch and pixel similarities in a multiplicative way. We perform experiments on real world images with additive white Gaussian noise (AWGN), and on electron microscopy data with a more general additive noise model. Our filter outperforms BM3D in 77% of the experiments, with improvements of up to 29% with respect to the mean squared error.
has issue date	2020-02-03(xsd:dateTime)
has license	arxiv
sha1sum (hex)	e80de126b3116600518381194bf1003bdee1c6b8
resource representing a document's title	Image denoising with less artefacts: Novel non-linear filtering on fast patch reorderings
resource representing a document's body	covid:e80de126b3116600518381194bf1003bdee1c6b8#body_text
is schema:about of	named entity 'matching' named entity 'artefacts' named entity 'EXPERIMENTS' named entity 'BLOCK' named entity 'SPECIFY' »more»

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