Range Scaling Global U-Net for Perceptual Image Enhancement on Mobile Devices

Jie Huang, Pengfei Zhu, Mingrui Geng, Jiewen Ran, Xingguang Zhou, Chen Xing, Pengfei Wan, Xiangyang Ji; Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0-0

Perceptual image enhancement on mobile devices—smart phones in particular—has drawn increasing industrial efforts and academic interests recently. Compared to digital single-lens reflex (DSLR) cameras, cameras on smart phones typically capture lower-quality images due to various hardware constraints. Without additional information, it is a challenging task to enhance the perceptual quality of a single image especially when the computation has to be done on mobile devices. In this paper we present a novel deep learning based approach—the Range Scaling Global U-Net (RSGUNet)—for perceptual image enhancement on mobile devices. Besides the U-Net structure that exploits image features at different resolutions, proposed RSGUNet learns a global feature vector as well as a novel range scaling layer that alleviate artifacts in the enhanced images. Extensive experiments show that the RSGUNet not only outputs enhanced images with higher subjective and objective quality, but also takes less inference time. Our proposal wins the 1st place by a great margin in track B of the Perceptual Image Enhancement on Smartphones Challenge (PRIM2018). Code is available at https://github.com/MTlab/ECCV-PIRM2018.