Non local means image filtering using clustering

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Resumen

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.

Idioma originalInglés
Título de la publicación alojadaProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 22nd Iberoamerican Congress, CIARP 2017, Proceedings
EditoresSergio Velastin, Marcelo Mendoza
EditorialSpringer Verlag
Páginas483-490
Número de páginas8
ISBN (versión impresa)9783319751924
DOI
EstadoPublicada - 2018
Evento22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017 - Valparaiso
Duración: 7 nov. 201710 nov. 2017

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen10657 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017
País/TerritorioChile
CiudadValparaiso
Período7/11/1710/11/17

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