Implementing swarm intelligence for image enhancement: a comparative study

被引：0
|
作者：
Riyazbanu S. [1 ]
Jena S.P. [2 ]
Pramanik J. [3 ]
Paikaray B.K. [4 ]
Samal A.K. [5 ]
机构：
[1] Department of CSE, KSRM College of Engineering, Andhra Pradesh, Kadapa
[2] Department of ECE, Centurion University of Technology and Management, Odisha
[3] Department of Mining, National Institute of Technology, Odisha, Rourkela
[4] Center for Data Science, SOA University, Odisha
[5] Department of CSE, Trident Academy of Technology, Odisha, Bhubaneswar
来源：
International Journal of Reasoning-based Intelligent Systems | 2024年 / 16卷 / 02期
关键词：
edge content; entropy; image enhancement; particle swarm optimisation algorithms; QPSO; quantum particle swarm optimisation;
D O I：
10.1504/IJRIS.2024.138629
中图分类号：
学科分类号：
摘要：
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. Copyright © 2024 Inderscience Enterprises Ltd.
引用
收藏
页码：160 / 169
页数：9
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