A comparative study for Image Enhancement using soft computing models

Our paper is based on the glimpse of comparative analysis of Image Enhancement techniques via different soft computing techniques, i.e Differential Evolution, Harmony Search, Bacterial Foraging Optimization and a hybrid Particle Swarm Adapted Bacterial Forgaing Optimization algorithm. Particle Swarm Adapted Bacterial Foraging (PS-BFO) is a new algorithm that has shown superior results in proportional integral derivative controller tuning application. In order to examine the global search capability of PS-BFO, we evaluate the performance of BFOA and PS-BFO on 23 numerical benchmark functions. In PS-BFO, the search directions of tumble behavior for each bacterium are oriented by the individual's best location and the global best location. The experimental results show that PS-BFO performs much better than BFOA for almost all test functions. That's approved that the PSO oriented BFO by strategy improve its global optimization capability. Results are compared with other recognition techniques like Differential Evolution, Harmony Search algorithm based image enhancement.