Abstract: Low-end and compact mobile cameras demonstrate limited photo quality mainly due to space, hardware and budget constraints. In this work, we propose a deep learning solution that translates photos taken by cameras with limited capabilities into DSLR-quality photos automatically. We tackle this problem by introducing a weakly supervised photo enhancer (WESPE) - a novel image-to-image GAN-based architecture. The proposed model is trained by weakly supervised learning: unlike previous works, there is no need for strong supervision in the form of a large annotated dataset of aligned original/enhanced photo pairs. The sole requirement is two distinct datasets: one from the source camera, and one composed of arbitrary high-quality images - the visual content they exhibit may be unrelated. Hence, our solution is repeatable for any camera: collecting the data and training can be achieved in a couple of hours. Our experiments on the DPED, Kitti and Cityscapes datasets as well as on photos from several generations of smartphones demonstrate that WESPE produces comparable qualitative results with state-of-the-art strongly supervised methods.

arXiv: 1709.01118, 2017 view the follow-up paper >>