A novel enhanced cuckoo search algorithm for contrast enhancement of gray scale images

A good contrast image has a significant role in different image processing applications and computer vision algorithms. One of the most common contrast enhancement approaches is histogram equalization (HE) that enhances the contrast of an image globally. However, it gives rise to some over-enhanced regions, loss of detail information, and enhancement of noise. In order to improve the performance of the HE algorithm, local HE and adaptive HE algorithms have been proposed but with limited success. Recently, an evolutionary algorithm named cuckoo search (CS) algorithm has been employed for automatic image contrast enhancement showing promising performance. In this paper, we propose a novel enhanced cuckoo search (ECS) algorithm for image contrast enhancement. In addition, we propose a new range of search space for the parameters of the local/global enhancement (LGE) transformation that need to be optimized. The proposed ECS algorithm is applied to several low contrast test images and its performance is compared with that of the CS algorithm. Next, we compare the performance of the ECS algorithm with artificial bee colony algorithm using the proposed LGE transformation and a global transformation. In the last stage of performance evaluation, the ECS algorithm is compared with several image enhancement algorithms, namely, HE, CLAHE, Particle Swarm Optimization (PSO), CS, modified CS and CS-PSO algorithms. In all cases, we have shown the superiority of the ECS algorithm in terms of several performance measures.