The implementation of optimization methods for contrast enhancement

Ahmet Elblr*, Hamza Osman Llhan, Nizamettin Aydin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

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. Article 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.

Original languageEnglish
Pages (from-to)101-107
Number of pages7
JournalComputer Systems Science and Engineering
Volume34
Issue number2
Publication statusPublished - 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019 CRL Publishing. All rights reserved.

Keywords

  • Artificial bee colony (abo
  • Artificial intelligent. particle swarm optimization (pso)
  • Histogram equalization
  • Histogram stretching
  • Image enhancement
  • Optimization

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