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2016 | OriginalPaper | Buchkapitel

Gaussian Noise Reduction Using Fuzzy Morphological Amoebas

verfasst von : Manuel González-Hidalgo, Sebastia Massanet, Arnau Mir, Daniel Ruiz-Aguilera

Erschienen in: Information Processing and Management of Uncertainty in Knowledge-Based Systems

Verlag: Springer International Publishing

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Abstract

Many image processing and computer vision applications require a preprocessing of the image to remove or reduce noise. Gaussian noise is a challenging type of noise whose removal has led to the proposal of several noise filters. In this paper we present a novel version of the morphological filters based on amoebas with the aim to incorporate fuzzy logic into them to achieve a better treatment of the uncertainty. The experimental results show that the proposed algorithm outperforms the classical amoeba-based filters both from the visual point of view and the quantitative performance values for images corrupted with Gaussian noise with standard deviation from 10 to 30.

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Vorheriges Kapitel Higher Degree F-transforms Based on B-splines of Two Variables

Nächstes Kapitel Proximal Optimization for Fuzzy Subspace Clustering

This image database can be downloaded from http://sipi.usc.edu/database/misc.tar.gz.

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Titel: Gaussian Noise Reduction Using Fuzzy Morphological Amoebas
verfasst von: Manuel González-Hidalgo
Sebastia Massanet
Arnau Mir
Daniel Ruiz-Aguilera
Verlag: Springer International Publishing
Buch: Information Processing and Management of Uncertainty in Knowledge-Based Systems
Print ISBN: 978-3-319-40595-7

Electronic ISBN: 978-3-319-40596-4

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-40596-4_55

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