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The Implementation of Optimization Methods for Contrast Enhancement

Ahmet Elbir1,∗, Hamza Osman Ilhan1, Nizamettin Aydin1
1 Department of Computer Engineering, Faculty of Electrical and Electronics Engineering, Yildiz Technical University, Istanbul, Turkey
* Corresponding Author: E-mail:

Computer Systems Science and Engineering 2019, 34(2), 101-107. https://doi.org/10.32604/csse.2019.34.101

Abstract

The performances of the multivariate techniques are directly related to the variable selection process, which is time consuming and requires resources for testing each possible parameter to achieve the best results. Therefore, optimization methods for variable selection process have been proposed in the literature to find the optimal solution in short time by using less system resources. Contrast enhancement is the one of the most important and the parameter dependent image enhancement technique. In this study, two optimization methods are employed for the variable selection for the contrast enhancement technique. Particle swarm optimization (PSO) and artificial bee colony (ABC) optimization methods are implemented to the histogram stretching technique in parameter selection process. The results of the optimized histogram stretching technique are compared with one of the parameter independent contrast enhancement technique; histogram equalization. The results show that the performance of the optimized histogram stretching is better not only in distorted images but also in original images. Histogram equalization degraded the original images while the optimized histogram stretching has no effect due to being an adaptive solution.

Keywords

Image enhancement, optimization, artificial intelligent, Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), histogram stretching, histogram equalization

Cite This Article

A. Elbir, H. Osman Ilhan and N. Aydin, "The implementation of optimization methods for contrast enhancement," Computer Systems Science and Engineering, vol. 34, no.2, pp. 101–107, 2019.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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