AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
It addresses the problem of realistic image degradation for super-resolution, which is crucial for real-world applications, but is incremental as it builds on existing challenge frameworks.
The paper introduced a challenge on real image super-resolution with three scaling tracks to address realistic image degradation, attracting 452 registrants and 24 submissions, and gauged state-of-the-art methods using PSNR and SSIM metrics.
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This challenge involves three tracks to super-resolve an input image for $\times$2, $\times$3 and $\times$4 scaling factors, respectively. The goal is to attract more attention to realistic image degradation for the SR task, which is much more complicated and challenging, and contributes to real-world image super-resolution applications. 452 participants were registered for three tracks in total, and 24 teams submitted their results. They gauge the state-of-the-art approaches for real image SR in terms of PSNR and SSIM.