NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

Yawei Li, Computer Vision Lab, ETH Zurich, Switzerland & University of Würzburg, Germany & ByteDance, Shenzhen, China
Kai Zhang, Computer Vision Lab, ETH Zurich, Switzerland & University of Würzburg, Germany
Radu Timofte, Computer Vision Lab, ETH Zurich, Switzerland & University of Würzburg, Germany
Luc Van Gool, Computer Vision Lab, ETH Zurich, Switzerland & University of Würzburg, Germany
Fangyuan Kong, ByteDance, Shenzhen, China
Mingxi Li, ByteDance, Shenzhen, China
Songwei Liu, ByteDance, Shenzhen, China
Zongcai Du, State Key Laboratory for Novel Software Technology, Nanjing University, China & ByteDance Inc, China
Ding Liu, State Key Laboratory for Novel Software Technology, Nanjing University, China & ByteDance Inc, China
Chenhui Zhou, NetEase, Inc., China & East China Normal University, China
Jingyi Chen, NetEase, Inc., China
Qingrui Han, NetEase, Inc., China

Abstract

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution. © 2022, CC BY.