Gu, ShuhangDanelljan, MartinTimofte, RaduHaris, MuhammadAkita, KazutoshiShakhnarovic, GregUkita, NorimichiMichelini, Pablo NavarreteChen, WenbinLiu, HanwenZhu, DanXie, TangxinYang, XinZhu, ChenYu, JiaSun, WenyuTao, XinDeng, ZijunLu, LiyingLi, WenboGuo, TaianShen, XiaoyongXu, XuemiaoTai, Yu-WingJia, JiayaYi, PengWang, ZhongyuanJiang, KuiJiang, JunjunMa, JiayiLiu, Zhi-SongWang, Li-WenLi, Chu-TakSiu, Wan-ChiChan, Yui-LamZhou, RuofanHelou, Majed EiPurohit, KuldeepKandula, PraveenSuin, MaitreyaRajagopalan, A. N.2020-09-042020-09-042020-09-042019-01-0110.1109/ICCVW.2019.00440https://infoscience.epfl.ch/handle/20.500.14299/171359WOS:000554591603090This paper reviews the AIM 2019 challenge on extreme image super-resolution, the problem of restoring of rich details in a low resolution image. Compared to previous, this challenge focuses on an extreme upscaling factor, x16, and employs the novel DIVerse 8K resolution (DIV8K) dataset. This report focuses on the proposed solutions and final results. The challenge had 2 tracks. The goal in Track 1 was to generate a super-resolution result with high fidelity, using the conventional PSNR as the primary metric to evaluate different methods. Track 2 instead focused on generating visually more pleasant super-resolution results, evaluated using subjective opinions. The two tracks had 71 and 52 registered participants, respectively, and 9 teams competed in the final testing phase. This report gauges the experimental protocol and baselines for the extreme image super-resolution task.text::conference output::conference proceedings::conference paper