DarkGAN: Night image enhancement using generative adversarial networks

Abstract

Low light image enhancement is one of the challenging tasks in computer vision, and it becomes more difficult when images are very dark. Recently, most of low light image enhancement work is done either on synthetic data or on the images which are considerably visible. In this paper, we propose a method to enhance real-world night time images, which are dark and noisy. The proposed DarkGAN consists of two pairs of Generator - Discriminator. Moreover, the proposed network enhances dark shades and removes noise up to a much extent, with natural-looking colors in the output image. Experimental results evaluation of the proposed method on the “See In the Dark” dataset demonstrates the effectiveness of the proposed model compared with other state-of-the-art methods. The proposed method yields comparable better results on qualitative and quantitative assessments when compared with the existing methods.