  Low-light image enhancement exhibits an ill-posed nature, as a given image
may have many enhanced versions, yet recent studies focus on building a
deterministic mapping from input to an enhanced version. In contrast, we
propose a lightweight one-path conditional generative adversarial network
(cGAN) to learn a one-to-many relation from low-light to normal-light image
space, given only sets of low- and normal-light training images without any
correspondence. By formulating this ill-posed problem as a modulation code
learning task, our network learns to generate a collection of enhanced images
from a given input conditioned on various reference images. Therefore our
inference model easily adapts to various user preferences, provided with a few
favorable photos from each user. Our model achieves competitive visual and
quantitative results on par with fully supervised methods on both noisy and
clean datasets, while being 6 to 10 times lighter than state-of-the-art
generative adversarial networks (GANs) approaches.
