Supervised and Unsupervised Learning of Parameterized Color Enhancement
This work addresses color enhancement for photographers and image processing applications, presenting an incremental improvement over existing methods.
The paper tackles color enhancement as an image translation task using parameterized color transformations, achieving state-of-the-art results on the MIT-Adobe FiveK benchmark for both supervised and unsupervised methods, with demonstrated generalization to historical photos and dark video frames.
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.