This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tracks. In the first one, participants were solving the classical image super-resolution problem with a bicubic downscaling factor of 4. The second track was aimed at real-world photo enhancement, and the goal was to map low-quality photos from the iPhone 3GS device to the same photos captured with a DSLR camera. The target metric used in this challenge combined the runtime, PSNR scores and solutions' perceptual results measured in the user study. To ensure the efficiency of the submitted models, we additionally measured their runtime and memory requirements on Android smartphones. The proposed solutions significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.
@article{arxiv.1810.01641,
title = {PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report},
author = {Andrey Ignatov and Radu Timofte and Thang Van Vu and Tung Minh Luu and Trung X Pham and Cao Van Nguyen and Yongwoo Kim and Jae-Seok Choi and Munchurl Kim and Jie Huang and Jiewen Ran and Chen Xing and Xingguang Zhou and Pengfei Zhu and Mingrui Geng and Yawei Li and Eirikur Agustsson and Shuhang Gu and Luc Van Gool and Etienne de Stoutz and Nikolay Kobyshev and Kehui Nie and Yan Zhao and Gen Li and Tong Tong and Qinquan Gao and Liu Hanwen and Pablo Navarrete Michelini and Zhu Dan and Hu Fengshuo and Zheng Hui and Xiumei Wang and Lirui Deng and Rang Meng and Jinghui Qin and Yukai Shi and Wushao Wen and Liang Lin and Ruicheng Feng and Shixiang Wu and Chao Dong and Yu Qiao and Subeesh Vasu and Nimisha Thekke Madam and Praveen Kandula and A. N. Rajagopalan and Jie Liu and Cheolkon Jung},
journal= {arXiv preprint arXiv:1810.01641},
year = {2018}
}