RGNET: A Two-stage Low-light Image Enhancement Network Without Paired Supervision

Deep learning-based methods have achieved remarkable success in low-light image enhancement. However, in the absence of a large number of low/normal light image pairs, it is still a challenge to train the enhancement network with good generalization ability. In this paper, we propose a highly effective unsupervised network for low-light image enhancement (named RGNET). We divide the enhancement task into two stages, complete from coarse to precise. At the first stage, we roughly amplify the input image nonlinearly using an unsupervised network. At the second stage, we build a two-path network to restore image details, one is uesed for residual restoration and the other is used for contextual attention. With the combination of reconstruction and adversarial loss, our enhancement effects are more consistant and natural than other GAN-based methods. Both quantitative and qualitative experiments on challenging datasets demonstrate the advantages of our method in comparison with state-of-the-art methods.