Particle swarm optimisation enhancement approach for improving image quality

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.

[1]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[2]  Agostinho C. Rosa,et al.  Towards automatic image enhancement using genetic algorithms , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[3]  M D Fox,et al.  Enhancement of chest radiographs with gradient operators. , 1988, IEEE transactions on medical imaging.

[4]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[5]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[6]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[7]  Te-Jen Su,et al.  Particle Swarm Optimization for Image Noise Cancellation , 2006, ICICIC.

[8]  David E. Goldberg,et al.  The Design of Innovation: Lessons from and for Competent Genetic Algorithms , 2002 .

[9]  Tim M. Blackwell,et al.  When is a Swarm Necessary? , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[10]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[11]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  M.C. Batouche,et al.  Particle swam optimization for image registration , 2004, Proceedings. 2004 International Conference on Information and Communication Technologies: From Theory to Applications, 2004..

[14]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[15]  Hongguang Sun,et al.  PSO based Gabor wavelet feature extraction method , 2004, International Conference on Information Acquisition, 2004. Proceedings..

[16]  Keiichiro Yasuda,et al.  Adaptive particle swarm optimization using velocity information of swarm , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[17]  Alaa Sheta,et al.  Reliability Growth Modeling for Software Fault Detection Using Particle Swarm Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[18]  Yongling Zheng,et al.  On the convergence analysis and parameter selection in particle swarm optimization , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[19]  Liyan Zhang,et al.  Empirical study of particle swarm optimizer with an increasing inertia weight , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[20]  Paul L. Rosin.  Edges: saliency measures and automatic thresholding , 1997, Machine Vision and Applications.

[21]  Talal M. Alkhamis,et al.  Simulation-based optimization for repairable systems using particle swarm algorithm , 2005, Proceedings of the Winter Simulation Conference, 2005..

[22]  Jiann-Horng Lin,et al.  Dynamic clustering using support vector learning with particle swarm optimization , 2005, 18th International Conference on Systems Engineering (ICSEng'05).

[23]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[24]  Ganesh K. Venayagamoorthy,et al.  Online Training of a Generalized Neuron with Particle Swarm Optimization , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[25]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[26]  Sanjit K. Mitra,et al.  Nonlinear unsharp masking methods for image contrast enhancement , 1996, J. Electronic Imaging.