Particle swarm optimisation enhancement approach for improving image quality

Particle Swarm Optimisation (PSO) algorithm represents anew 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.