An Adaptive Image Enhancement Technique Based on Firefly Algorithm

Image enhancement is an important step in image processing, and the enhancement approach based on normalization of incomplete beta function is able to achieve ideal enhanced results. However, it often requires human intervention or is time-consuming to obtain good parameters, which still remains an issue not fully solved. In practice, it is an optimization problem to select the optimal parameters for the method. In the paper, the Firefly Algorithm(FA) is employed to search for the optimal parameters and the acquired optimal parameters are used to generate the gray level transformation curve to improve images. The performance of the proposed method is contrasted with other evolutionary computing methods like genetic algorithm and particle swarm optimization algorithm. Experimental results display that FA is able to gain the optimal parameters effectively and adaptively, which outperforms the other optimization algorithms involved in the paper.