Non local means image filtering using clustering

Alvaro Pardo

doi:10.1007/978-3-319-75193-1_58

Non local means image filtering using clustering

Alvaro Pardo

Department of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In this work we study improvements for the Non Local Means image filter using clustering in the space of patches. Patch clustering it is proposed to guide the selection of the best patches to be used to filter each pixel. Besides clustering, we incorporate spatial coherence keeping some local patches extracted from a local search window around each pixel. Therefore, for each pixel we use local patches and non local ones extracted from the corresponding cluster. The proposed method outperforms classical Non Local Means filter and also, when compared with other methods from the literature based on random sampling, our results confirm the benefits of sampling inside clusters combined with local information.

Original language	English
Title of host publication	Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 22nd Iberoamerican Congress, CIARP 2017, Proceedings
Editors	Sergio Velastin, Marcelo Mendoza
Publisher	Springer Verlag
Pages	483-490
Number of pages	8
ISBN (Print)	9783319751924
DOIs	https://doi.org/10.1007/978-3-319-75193-1_58
State	Published - 2018
Event	22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017 - Valparaiso, Chile Duration: 7 Nov 2017 → 10 Nov 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10657 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017
Country/Territory	Chile
City	Valparaiso
Period	7/11/17 → 10/11/17

Access to Document

10.1007/978-3-319-75193-1_58

Cite this

Pardo, A. (2018). Non local means image filtering using clustering. In S. Velastin, & M. Mendoza (Eds.), Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 22nd Iberoamerican Congress, CIARP 2017, Proceedings (pp. 483-490). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10657 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-75193-1_58

Pardo, Alvaro. / Non local means image filtering using clustering. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 22nd Iberoamerican Congress, CIARP 2017, Proceedings. editor / Sergio Velastin ; Marcelo Mendoza. Springer Verlag, 2018. pp. 483-490 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this work we study improvements for the Non Local Means image filter using clustering in the space of patches. Patch clustering it is proposed to guide the selection of the best patches to be used to filter each pixel. Besides clustering, we incorporate spatial coherence keeping some local patches extracted from a local search window around each pixel. Therefore, for each pixel we use local patches and non local ones extracted from the corresponding cluster. The proposed method outperforms classical Non Local Means filter and also, when compared with other methods from the literature based on random sampling, our results confirm the benefits of sampling inside clusters combined with local information.",

author = "Alvaro Pardo",

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Pardo, A 2018, Non local means image filtering using clustering. in S Velastin & M Mendoza (eds), Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 22nd Iberoamerican Congress, CIARP 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10657 LNCS, Springer Verlag, pp. 483-490, 22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017, Valparaiso, Chile, 7/11/17. https://doi.org/10.1007/978-3-319-75193-1_58

Non local means image filtering using clustering. / Pardo, Alvaro.
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 22nd Iberoamerican Congress, CIARP 2017, Proceedings. ed. / Sergio Velastin; Marcelo Mendoza. Springer Verlag, 2018. p. 483-490 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10657 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N1 - Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018.

PY - 2018

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N2 - In this work we study improvements for the Non Local Means image filter using clustering in the space of patches. Patch clustering it is proposed to guide the selection of the best patches to be used to filter each pixel. Besides clustering, we incorporate spatial coherence keeping some local patches extracted from a local search window around each pixel. Therefore, for each pixel we use local patches and non local ones extracted from the corresponding cluster. The proposed method outperforms classical Non Local Means filter and also, when compared with other methods from the literature based on random sampling, our results confirm the benefits of sampling inside clusters combined with local information.

AB - In this work we study improvements for the Non Local Means image filter using clustering in the space of patches. Patch clustering it is proposed to guide the selection of the best patches to be used to filter each pixel. Besides clustering, we incorporate spatial coherence keeping some local patches extracted from a local search window around each pixel. Therefore, for each pixel we use local patches and non local ones extracted from the corresponding cluster. The proposed method outperforms classical Non Local Means filter and also, when compared with other methods from the literature based on random sampling, our results confirm the benefits of sampling inside clusters combined with local information.

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Pardo A. Non local means image filtering using clustering. In Velastin S, Mendoza M, editors, Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 22nd Iberoamerican Congress, CIARP 2017, Proceedings. Springer Verlag. 2018. p. 483-490. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-75193-1_58

Non local means image filtering using clustering

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