Particle swarm optimisation enhancement approach for improving image quality. (23rd January 2008)

• Record Type: Journal Article
• Authors: Braik, Malik; Sheta, Alaa; Ayesh, Aladdin
• Is Part Of: International journal of innovative computing and applications. Volume 1:Number 2(2007)
• Page Start: 138
• Page End: 145
• Publication Date: 2008-01-23
• Subjects: image enhancement -- image quality -- particle swarm optimisation -- PSO -- genetic algorithms -- GAs -- grey scale images; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Particle Swarm Optimisation (PSO) algorithm represents a new approach to optimisation problems. In this paper, image enhancement is presented as an optimisation problem to which PSO is applied. This application is done within a nouvelle automatic image enhancement technique encompassing a real-coded particle swarms algorithm. The enhancement process is a non-linear optimisation problem with several constraints. Based upon a mathematical model of the social interactions of swarms, the algorithm has been shown to be effective at finding good solutions of the enhancement problem by adapting the parameters of a novel extension to a local enhancement technique similar to statistical scaling. This enhances the contrast and detail in the image according to an objective fitness criterion. The proposed algorithm has been compared with Genetic Algorithms (GAs) to a number of tested images. The obtained results using grey scale images indicate that PSO is better than GAs in terms of the computational time and both the objective evaluation and maximisation of the number of pixels in the edges of the tested images.