Performance Comparison of Image Enhancers with and without Deep Learning

Image enhancement is a computational procedure to improve visibility of details and content of an input image. Several image enhancement algorithms have been developed thus far, from traditional methods that process a single image based on physical models of image acquisition and formation to recent deep learning techniques, where enhancement models are learned from data. Here, we empirically compare a set of traditional and deep learning enhancers, which we selected as representing different methodologies for the improvement of poorly illuminated images. Our experiments are conducted on public data and show that, although all the considered enhancers improve the visibility of the image content and details, the deep-learning-based methods generally produce less noisy images than the traditional ones. This last outcome has to be carefully considered when enhancers are used as preprocessing for algorithms that are sensitive to noise. As a case study, and with the purpose to promote more aware usage of these two groups of enhancers in computer vision applications, we discuss the impact of image enhancement in the framework of image retrieval performed through two popular algorithms, i.e., SIFT and ORB, implementing different image descriptions and having different sensitivities to noise.