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Soft morphological filter optimization using a genetic algorithm for noise elimination

Ercal, Turker; Özcan, Ender; Asta, Shahriar

Authors

Turker Ercal turker.ercal@gmail.com

Ender Özcan exo@cs.nott.ac.uk

Shahriar Asta sba@cs.nott.ac.uk



Abstract

Digital image quality is of importance in almost all image processing applications. Many different approaches have been proposed for restoring the image quality depending on the nature of the degradation. One of the most common problems that cause such degradation is impulse noise. In general, well known median filters are preferred for eliminating different types of noise. Soft morphological filters are recently introduced and have been in use for many purposes. In this study, we present a Genetic Algorithm (GA) which combines different objectives as a weighted sum under a single evaluation function and generates a soft morphological filter to deal with impulse noise, after a training process with small images. The automatically generated filter performs better than the median filter and achieves comparable results to the best known filters from the literature over a set of benchmark instances that are larger than the training instances. Moreover, although the training process involves only impulse noise added images, the same evolved filter performs better than the median filter for eliminating Gaussian noise as well.

Start Date Sep 8, 2014
Publication Date Oct 20, 2014
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Book Title 2014 14th UK Workshop on Computational Intelligence (UKCI)
APA6 Citation Ercal, T., Özcan, E., & Asta, S. (2014). Soft morphological filter optimization using a genetic algorithm for noise elimination. In 2014 14th UK Workshop on Computational Intelligence (UKCI)doi:10.1109/UKCI.2014.6930177
DOI https://doi.org/10.1109/UKCI.2014.6930177
Publisher URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6930177
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information Published in: 2014 14th UK Workshop on Computational Intelligence (UKCI). IEEE, 2014, ISBN, 978-1-4799-5538-1. pp. 1-7, doi: 10.1109/UKCI.2014.6930177.

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