AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

TL;DR

This paper reviews the AIM 2020 challenge on efficient super-resolution, highlighting innovative methods and results aimed at balancing high image quality with computational efficiency.

Contribution

It provides a comprehensive overview of the challenge solutions, focusing on techniques that optimize efficiency while maintaining super-resolution quality.

Findings

25 teams submitted final solutions

New efficient super-resolution models achieved competitive PSNR

The challenge set new benchmarks for runtime and parameter efficiency

Abstract

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.