An approach based on hybrid genetic algorithm applied to image denoising problem

Author(s)

de Paiva, Jonatas Lopes ^[1] ; Toledo, Claudio F. M. ^[1] ; Pedrini, Helio ^[2]

Affiliation

^[1] Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Carlos, SP - Brazil

^[2] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP - Brazil

• Document type: Journal article
• Source: APPLIED SOFT COMPUTING; v. 46, p. 778-791, SEP 2016.
• Web of Science Citations: 8

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. (AU)

• FAPESP's process: 11/22749-8 - Challenges in exploratory visualization of multidimensional data: paradigms, scalability and applications
• Grantee: Luis Gustavo Nonato
• Support Opportunities: Research Projects - Thematic Grants