PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
This addresses the problem of deploying efficient deep learning models for image enhancement on smartphones, but it is incremental as it builds on existing challenges and methods.
The paper reviewed a challenge on efficient perceptual image enhancement for smartphones, with two tracks: image super-resolution and real-world photo enhancement, resulting in solutions that significantly improved baseline results and set new state-of-the-art benchmarks.
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.