  Deep learning-based methods have achieved remarkable success in image
restoration and enhancement, but are they still competitive when there is a
lack of paired training data? As one such example, this paper explores the
low-light image enhancement problem, where in practice it is extremely
challenging to simultaneously take a low-light and a normal-light photo of the
same visual scene. We propose a highly effective unsupervised generative
adversarial network, dubbed EnlightenGAN, that can be trained without
low/normal-light image pairs, yet proves to generalize very well on various
real-world test images. Instead of supervising the learning using ground truth
data, we propose to regularize the unpaired training using the information
extracted from the input itself, and benchmark a series of innovations for the
low-light image enhancement problem, including a global-local discriminator
structure, a self-regularized perceptual loss fusion, and attention mechanism.
Through extensive experiments, our proposed approach outperforms recent methods
under a variety of metrics in terms of visual quality and subjective user
study. Thanks to the great flexibility brought by unpaired training,
EnlightenGAN is demonstrated to be easily adaptable to enhancing real-world
images from various domains. The code is available at
\url{https://github.com/yueruchen/EnlightenGAN}
