DeepSelfie: Single-Shot Low-Light Enhancement for Selfies

Taking a high-quality selfie photo in a low-light environment is challenging. Because the foreground and background often have different illumination conditions, they suffer heavily from over/under-exposure issues and cannot be treated in the same manner when applying image enhancement algorithms. In this work, we propose DeepSelfie, a learning-based image enhancement framework for low-light selfie photos. We address selfie enhancement as a dual-layer image enhancement problem. The foreground and background are thus separately enhanced and combined together via image fusion. To train the selfie enhancement network, we also introduce a method of synthesizing pairs of noisy and dark raw selfie images and their corresponding well-illuminated images. Through extensive experiments of no-reference image quality assessment as well as human subjective evaluation, we show that DeepSelfie provides better results in comparison to several state-of-the-art methods. The code and datasets can be found at https://sites.google.com/view/deepselfie.