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.　©　Springer　Nature　Switzerland　AG　2019.