Fundamentals of digital image processing
Fundamentals of digital image processing
Edges: saliency measures and automatic thresholding
Machine Vision and Applications
Swarm intelligence
Introductory Digital Image Processing: A Remote Sensing Perspective
Introductory Digital Image Processing: A Remote Sensing Perspective
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Dynamic Clustering Using Support Vector Learning with Particle Swarm Optimization
ICSENG '05 Proceedings of the 18th International Conference on Systems Engineering
Particle Swarm Optimization for Image Noise Cancellation
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1
Simulation-based optimization for repairable systems using particle swarm algorithm
WSC '05 Proceedings of the 37th conference on Winter simulation
Remote Sensing and Image Interpretation
Remote Sensing and Image Interpretation
Particle swarm based unsharp masking
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
An evolutionary Lagrange method for batch process optimal design
International Journal of Innovative Computing and Applications
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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.