NTIRE 2019 Challenge on Real Image Super-Resolution: Methods and Results

This paper reviewed the 3rd NTIRE challenge on single-image super-resolution (restoration of rich details in a low-resolution image) with a focus on proposed solutions and results. The challenge had 1 track, which was aimed at the real-world single image super-resolution problem with an unknown scaling factor. Participants were mapping low-resolution images captured by a DSLR camera with a shorter focal length to their high-resolution images captured at a longer focal length. With this challenge, we introduced a novel real-world super-resolution dataset (RealSR). The track had 403 registered participants, and 36 teams competed in the final testing phase. They gauge the state-of-the-art in real-world single image super-resolution.

1. Introduction

Single image super-resolution (SISR) [13] aims to restore a high-resolution (HR) image from its low-resolution (LR) observation. SISR has been an active research topic for decades [29], [44], [34], [36], [1] because of its high practical values in enhancing image details and textures. Since SISR is a severely ill-posed inverse problem, for each LR image the space of plausible corresponding HR images is huge and scales up quadratically with the magnification factor, learning image prior information from the HR and/or LR exemplar images [13], [10], [42], [15], [11], [3], [20], [43], [8], [16], [35], [31] plays an indispensable role in recovering the details from an LR input image.