Cuckoo search optimization-based image color and detail enhancement for contrast distorted images

Researchers have developed various contrast enhancement techniques to provide perceptually good quality images for many vision-based applications. An exposure-based optimally weighted gamma correction framework is proposed in this work to overcome the drawback of low contrast and unclear detail in contrast distorted image. At first, the intensities of the input image are expanded and compressed to increase the fine details and reduce the viewable artifacts. The gamma correction and weighted sum approach are then introduced to provide good contrast and natural-looking images with optimal gamma values. Besides, the cuckoo search algorithm is employed to calculate the optimum value of the gamma function. In this work, the mean squared error is used as the objective function to maximize the information content of the enhanced image. The simulation results reveal the efficiency of the proposed method in enhancing the image for various levels of contrast distortion. Besides, quantitative and qualitative comparisons with the recently published methods demonstrate the superiority of the proposed algorithm in improving details and natural color of the contrast distorted images.