Low-light Image Enhancement Using Chain-consistent Adversarial Networks

The capability to generate clear and bright images in low light situations is crucial for photographers, engineers, and researchers. When it is not possible to modify the imaging conditions, an algorithm to enhance images is needed. Traditional methods require manually adjusting parameters to tune the image. Supervised learning methods need to collect a large amount of paired data for training. In this paper, we demonstrate an semi-supervised method for low light image enhancement, using a chain of cycle consistent generators. We show the effectiveness of our method by comparing it to existing image enhancement methods, both using standard image quality metrics and by using human perceptual judgements. We include an ablation study for features in our model. Our proposed method is computationally efficient and does not require paired training data.