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Image Enhancement using Ant Colony Optimization

Applications of the Ant Colony Optimization (ACO) to solve image processing problem with a reference to a new automatic enhancement technique based on real-coded particle ant colony is proposed in this paper. The enhancement process is a non-linear optimization problem with several constraints. The objective of the proposed ACO is to maximize an objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The feasibility of the proposed method is demonstrated and compared with Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) based image enhancement technique. The obtained results indicate that the proposed ACO yields better results in terms of both the maximization of the number of pixels in the edges and the adopted objective evaluation. Computational time is also relatively small in the ACO case compared to the GA and PSO case.