Abstract
Many practical applications require analysis of digital images. An accurate analysis is possible only from an image free of noise. Image denoising with multiple image filters might produce better results than a single filter, but it is difficult to find a set of appropriate filters and the order in which the filters are to be applied. In this paper, we propose a Fuzzy Genetic Algorithm to find the optimal filter sets for removing all types of impulse noise from grayscale images. Here, a Fuzzy Rule Base is used to adaptively change the crossover probability of the Genetic Algorithm used to determine the optimal filter sets. The results of simulations performed on a set of standard test images for a wide range of noise corruption levels shows that the proposed method outperforms standard procedures for impulse noise removal.
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Anisha, K.K., Wilscy, M. (2011). Impulse Noise Removal from Grayscale Images Using Fuzzy Genetic Algorithm. In: Nagamalai, D., Renault, E., Dhanuskodi, M. (eds) Advances in Parallel Distributed Computing. PDCTA 2011. Communications in Computer and Information Science, vol 203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24037-9_7
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DOI: https://doi.org/10.1007/978-3-642-24037-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24036-2
Online ISBN: 978-3-642-24037-9
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