A novel ranking algorithm of enhanced images using a convolutional neural network and a saliency-based patch selection scheme

A plethora of Contrast Enhancement (CE) methods has been proposed in the literature. Each of these has its own strengths and limitations. Further, the quality of the resulting enhanced images depends upon the original image and its content. Hence, a given CE method can provide good quality for a certain image but a poorer quality for another. In this paper, we propose a novel workflow to provide an automatic ranking of enhanced images which may have been obtained using different techniques. The proposed technique is based on a Convolutional Neural Network (CNN) using saliency information. The idea is to start by comparing two enhanced versions of a given image in order to select the best one automatically based on perceived quality. Here, a saliency map is used to select relevant patches which are highly correlated with the human visual system sensitivity. The well-known Structural Similarity Image Metric (SSIM) map is also employed to compare the similarity between both enhanced images. Using such information, a CNN model is trained to predict the rank in terms of the image quality as perceived by humans. The algorithm is tested over three CE benchmarking databases with the experimental results validating the superiority of the proposed system as compared to state-of-the-art CE evaluation techniques.