Invertible network for unpaired low-light image enhancement

Jize Zhang, Haolin Wang, Xiaohe Wu, Wangmeng Zuo

Abstract: Existing unpaired image enhancement approaches prefer to employ the traditional two-way generative adversarial network (GAN) framework, in which two convolutional neural network (CNN) generators are deployed for enhancement and degradation separately. However, such data-driven models ignore the inherent characteristics of transformation between the low-light and normal-light images, leading to unstable training and artifacts. Here, we propose to leverage the invertible neural network to enhance the low-light images in the forward process and degrade the unpaired normal-light photograph inversely. The generated and real images are then fed into discriminators for adversarial learning. In addition to the adversarial loss, we design transformation-consistent loss to ensure the stability of training, detail-preserving loss to preserve more image details, and reversibility loss to alleviate the over-exposure problem. Moreover, we present a progressive self-guided enhancement process in inference and achieve favorable performance against the state-of-the-art methods.