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Low-end and compact mobile cameras demonstrate limited photo quality mainlydue to space, hardware and budget constraints. In this work, we propose a deeplearning solution that translates photos taken by cameras with limitedcapabilities into DSLR-quality photos automatically. We tackle this problem byintroducing a weakly supervised photo enhancer (WESPE) - a novel image-to-imageGenerative Adversarial Network-based architecture. The proposed model istrained by under weak supervision: unlike previous works, there is no need forstrong supervision in the form of a large annotated dataset of alignedoriginal/enhanced photo pairs. The sole requirement is two distinct datasets:one from the source camera, and one composed of arbitrary high-quality imagesthat can be generally crawled from the Internet - the visual content theyexhibit may be unrelated. Hence, our solution is repeatable for any camera:collecting the data and training can be achieved in a couple of hours. In thiswork, we emphasize on extensive evaluation of obtained results. Besidesstandard objective metrics and subjective user study, we train a virtual raterin the form of a separate CNN that mimics human raters on Flickr data and usethis network to get reference scores for both original and enhanced photos. Ourexperiments on the DPED, KITTI and Cityscapes datasets as well as pictures fromseveral generations of smartphones demonstrate that WESPE produces comparableor improved qualitative results with state-of-the-art strongly supervisedmethods.

WESPE: Weakly Supervised Photo Enhancer for Digital Cameras