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Type: Artigo
Title: An Approach Based On Hybrid Genetic Algorithm Applied To Image Denoising Problem
Author: de Paiva
Jonatas Lopes; Toledo
Claudio F. M.; Pedrini
Abstract: An approach based on hybrid genetic algorithm (HGA) is proposed for image denoising. In this problem, a digital image corrupted by a noise level must be recovered without losing important features such as edges, corners and texture. The HGA introduces a combination of genetic algorithm (GA) with image denoising methods. During the evolutionary process, this approach applies some state-of-the-art denoising methods and filtering techniques, respectively, as local search and mutation operators. A set of digital images, commonly used by the scientific community as benchmark, is contaminated by different levels of additive Gaussian noise. Another set composed of some Satellite Aperture Radar (SAR) images, corrupted with a multiplicative speckle noise, is also used during the tests. First, the computational tests evaluate several alternative designs from the proposed HGA. Next, our approach is compared against literature methods on the two mentioned sets of images. The HGA performance is competitive for the majority of the reported results, outperforming several state-of-the-art methods for images with high levels of noise. (C) 2015 Elsevier B.V. All rights reserved.
Subject: Image Denoising
Genetic Algorithm
Feature Preservation
Editor: Elsevier Science BV
Citation: Applied Soft Computing. Elsevier Science Bv, v. 46, p. 778 - 791, 2016.
Rights: fechado
Identifier DOI: 10.1016/j.asoc.2015.09.013
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

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