Perception-Preserving Convolutional Networks for Image Enhancement on Smartphones

Zheng Hui, Xiumei Wang, Lirui Deng, Xinbo Gao; Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0-0


Although the configuration of smartphone cameras is getting better and better, the quality of smartphone photos still cannot match DSLR camera photos due to the limitation of physical space, hardware and cost. In this work, we present a fast and accurate image enhancement approach based on generative adversarial nets, which elevates the quality of photos on smartphones. We propose the lightweight local residual convolutional network to learn the mapping between ordinary photos and DSLR-quality images. To make the generated images look real, we introduce the perception-preserving measurement error, which comprises content, color, and adversarial losses. Especially, the content loss is constituted of contextual and SSIM losses, which maintains the natural internal statistics and the structure of images. In addition, we introduce the knowledge transfer strategy to ensure the high performance of the proposed network. The experiments demonstrate that our proposed method produces better results compared with the state-of-the-art approaches, both qualitatively and quantitatively. The code is available at

Related Material

author = {Hui, Zheng and Wang, Xiumei and Deng, Lirui and Gao, Xinbo},
title = {Perception-Preserving Convolutional Networks for Image Enhancement on Smartphones},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) Workshops},
month = {September},
year = {2018}