Soft morphological filter optimization using a genetic algorithm for noise elimination

Ercal, Turker; �zcan, Ender; Asta, Shahriar

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.

Citation

Ercal, T., Özcan, E., & Asta, S. (2014, September). Soft morphological filter optimization using a genetic algorithm for noise elimination. Presented at UK Workshop on Computational Intelligence (UKCI2014), Bradford, UK

• Conference Name: UK Workshop on Computational Intelligence (UKCI2014)
• Start Date: Sep 8, 2014
• End Date: Sep 10, 2014
• Acceptance Date: Jul 11, 2014
• Publication Date: Oct 20, 2014
• Deposit Date: Mar 18, 2015
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Book Title: 2014 14th UK Workshop on Computational Intelligence (UKCI)
• DOI: https://doi.org/10.1109/UKCI.2014.6930177
• Public URL: https://nottingham-repository.worktribe.com/output/998394
• Publisher URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6930177
• 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.; ; © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.