Multi-Objective Image Enhancement Using Particle Swarm Optimization

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  • Mehmet Emin EROĞLU
  • Ömer Kaan BAYKAN


Image Processing, Metaheuristic Algorithm, Pareto Optimal Approach, PSO


Images sometimes become unusable due to adverse environment conditions. Image enhancement methods solve the problem.
There are miscellaneous methods for image enhancement operations such as gray scale modification, histogram equalization
and contrast stretching techniques. In addition, metaheuristic optimization techniques are used for image enhancement. Particle
Swarm Optimization (PSO) is one of the metaheuristic algorithms. In this study, firstly a transformation function was
determined for image enhancement from the literature. PSO was developed with Pareto Optimal approach as a multi-objective.
The improved PSO was used to determine optimal values of parameters in the function. The parameters are pretty influential
on transformation of images. Entropy and Contrast Improvement Index (CII) of obtained images were calculated for evaluation
process. Finally, the experimental outputs were compared with the results of another study.




How to Cite

EROĞLU, M. E., & BAYKAN, Ömer K. (2019). Multi-Objective Image Enhancement Using Particle Swarm Optimization. International Journal of Natural and Engineering Sciences, 12(1), 01–07. Retrieved from