Implementing swarm intelligence for image enhancement: a comparative study

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.