NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results

TL;DR

This paper reviews the NTIRE 2020 challenge on real-world image super-resolution, highlighting innovative methods and results in two tracks focused on artifact removal and smartphone image enhancement, aiming to improve perceptual quality.

Contribution

It presents a comprehensive overview of the challenge's participating methods and results, advancing the state-of-the-art in real-world super-resolution techniques.

Findings

22 teams participated, showcasing diverse innovative solutions.

Methods improved perceptual quality in real-world super-resolution.

Benchmark results established new performance standards.

Abstract

This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided along with a set of unpaired high-quality target images. In Track 1: Image Processing artifacts, the aim is to super-resolve images with synthetically generated image processing artifacts. This allows for quantitative benchmarking of the approaches \wrt a ground-truth image. In Track 2: Smartphone Images, real low-quality smart phone images have to be super-resolved. In both tracks, the ultimate goal is to achieve the best perceptual quality, evaluated using a human study. This is the second challenge on the subject, following AIM 2019, targeting to advance the state-of-the-art in super-resolution. To measure the performance we use the benchmark protocol from AIM 2019. In total 22 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem.