NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results

This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results. A new DIVerse 2K resolution image dataset (DIV2K) was employed. The challenge had 6 competitions divided into 2 tracks with 3 magnification factors each. Track 1 employed the standard bicubic downscaling setup, while Track 2 had unknown downscaling operators (blur kernel and decimation) but learnable through low and high res train images. Each competition had ∽100 registered participants and 20 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.

1. Introduction

Example-based single image super-resolution (SR) aims at the restoration of rich details (high frequencies) in an image based on a set of prior examples with low resolution (LR) and corresponding high resolution (HR) images. The loss in image content can be due causes such as quantization error, limitations of the sensor from the capturing camera, the presence of blur or other degrading operators and the use of downscaling operators to reduce the image resolution for storage purposes. SR is ill-posed, since for each LR image the space of corresponding HR images can be very large.