Soft morphological filter optimization using a genetic algorithm for noise elimination

Ercal, Turker and Özcan, Ender and Asta, Shahriar (2014) Soft morphological filter optimization using a genetic algorithm for noise elimination. In: 2014 14th UK Workshop on Computational Intelligence (UKCI), 8-10 September 2014, Bradford, Great Britain.

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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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published in: 2014 14th UK Workshop on Computational Intelligence (UKCI): Student Central Lecture Theatre SC0.51, University of Bradford, Bradford, West Yorkshire, UK : 8-10 September 2014 /editors, Daniel Neagu, Mariam Kiran, Paul Trundle. [Piscataway, N.J.] :IEEE,c2014, p. 1-7. ISBN: 9781479955381, doi: 10.1109/UKCI.2014.6930177
Keywords: Filter Design, Supervised Learning, Genetic Algo- rithm, Image Processing.
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1109/UKCI.2014.6930177
Depositing User: Ozcan, Dr Ender
Date Deposited: 27 Jun 2016 09:01
Last Modified: 14 Sep 2016 10:14
URI: http://eprints.nottingham.ac.uk/id/eprint/34403

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