Title: Implementing swarm intelligence for image enhancement: a comparative study

Authors: Shaik Riyazbanu; Swarna Prabha Jena; Jitendra Pramanik; Bijay Kumar Paikaray; Abhaya Kumar Samal

Addresses: Department of CSE, KSRM College of Engineering Kadapa, Andhra Pradesh, India ' Department of ECE, Centurion University of Technology and Management, Odisha, India ' Department of Mining, National Institute of Technology, Rourkela Odisha, India ' Center for Data Science, SOA University, Odisha, India ' Department of CSE, Trident Academy of Technology, Bhubaneswar, Odisha India

Abstract: Image enhancement improves visual image quality and plays a crucial part in computer vision and image processing. However, it is the numerous limitations to nonlinear optimisation issues. The goal of the current work is to demonstrate the adaptability and efficacy of different particle swarm optimisation algorithms in improving the contrast and detail of grayscale images, including PSO, standard PSO (SPSO), weight improved PSO (WIPSO), modified PSO (MPSO), and quantum PSO (QPSO). The optimum result is achieved by maximising the objective function criteria by controlling the transformation function parameters. The performance of the algorithms is measured and assessed through quality metric parameters such as the sum of edge intensities, edge information, entropy, fitness function, detailed variance, and background variance.

Keywords: particle swarm optimisation algorithms; quantum particle swarm optimisation; QPSO; entropy; edge content; image enhancement.

DOI: 10.1504/IJRIS.2024.138629

International Journal of Reasoning-based Intelligent Systems, 2024 Vol.16 No.2, pp.160 - 169

Received: 30 Sep 2022
Accepted: 28 Feb 2023
Published online: 18 May 2024 *

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