Standard Particle Swarm Optimization Algorithm for Image Enhancement

Mani Kumar Jogi*, Y. Srinivasa Rao**
*-** Department of Instrument Technology, Andhra University, Andhra Pradesh, India.
Periodicity:October - December'2021
DOI : https://doi.org/10.26634/jip.8.4.18441

Abstract

Image enhancement plays a crucial role in an image processing system. Improving image quality by maximizing the information content of a given input image is the primary goal of image enhancement. Many of the methods proposed earlier did not achieve a good improvement in quality. Optimization algorithms for solving the image enhancement problem are proposed. The quality of the input image is improved by choosing the optimal parameters based on the objective function formulated for the optimization process. The design of the objective function plays a crucial role in the optimization problem. This paper presents an efficient, objective approach to gray level image enhancement using the Standard Particle Swarm Optimization (SPSO) algorithm, which is an improvement on the simplest particle swarm optimization algorithm. The proposed algorithm has been tested on standard gray-level test images such as cat, stone, and eye. The proposed algorithm is evaluated based on two scenarios for improving the gray-level image and is successful in finding the optimal parameters for improving image quality.

Keywords

Image Enhancement, Standard PSO Algorithm, Image Quality Evaluation, Peak Signal-to-Noise Ratio, Root Mean Square Error.

How to Cite this Article?

Jogi, M. K., and Rao, Y. S. (2021). Standard Particle Swarm Optimization Algorithm for Image Enhancement. i-manager’s Journal on Image Processing, 8(4), 9-16. https://doi.org/10.26634/jip.8.4.18441

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