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NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

Radu Timofte; Shuhang Gu; Jiqing Wu; Luc Van Gool; Lei Zhang; Ming-Hsuan Yang

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1.
Introduction
2.
Ntire 2018 Challenge
3.
Challenge Results
4.
Challenge Methods and Teams
A.
Teams and Affiliations

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Abstract:

This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and r...Show More

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Abstract:

This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were learnable through provided pairs of low and high resolution train images. The tracks had 145, 114, 101, and 113 registered participants, resp., and 31 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.

Published in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Date of Conference: 18-22 June 2018

Date Added to IEEE Xplore: 16 December 2018

ISBN Information:

ISSN Information:

DOI: 10.1109/CVPRW.2018.00130

Conference Location: Salt Lake City, UT, USA

Citations are not available for this document.

1. Introduction

Example-based single image super-resolution (SR) targets the reconstruction of the lost high frequencies (rich details) in an image with the help of a set of prior examples of paired low resolution (LR) and high resolution (HR) images. This problem is ill-posed, for each LR image the space of plausible corresponding HR images is huge and scales up quadratically with the magnification factor.

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