  We treat the problem of color enhancement as an image translation task, which
we tackle using both supervised and unsupervised learning. Unlike traditional
image to image generators, our translation is performed using a global
parameterized color transformation instead of learning to directly map image
information. In the supervised case, every training image is paired with a
desired target image and a convolutional neural network (CNN) learns from the
expert retouched images the parameters of the transformation. In the unpaired
case, we employ two-way generative adversarial networks (GANs) to learn these
parameters and apply a circularity constraint. We achieve state-of-the-art
results compared to both supervised (paired data) and unsupervised (unpaired
data) image enhancement methods on the MIT-Adobe FiveK benchmark. Moreover, we
show the generalization capability of our method, by applying it on photos from
the early 20th century and to dark video frames.
