Arbitrary-scale Super-resolution via Deep Learning: A Comprehensive Survey

Super -resolution (SR) is an essential class of low-level vision tasks, which aims to improve the resolution of images or videos in computer vision. In recent years, significant progress has been made in image and video super -resolution techniques based on deep learning. Nevertheless, most of the methods only consider SR with a few integer scale factors, which limits the application of the SR techniques to real -world problems. Recently, the methods to achieve arbitrary -scale super -resolution via a single model have attracted much attention. However, there is no work to thoroughly analyze the arbitrary -scale methods based on deep learning. In this work, we present a comprehensive and systematic review of 45 existing deep learning -based methods for arbitrary -scale image and video SR. We first classify the existing SR methods according to the resolved scale factors. Furthermore, we propose an in-depth taxonomy for state-of-the-art methods based on the core problem of how to achieve arbitrary -scale super -resolution, i.e., how to perform arbitrary -scale upsampling. Moreover, the performance of existing arbitrary -scale SR methods is compared, and their advantages and limitations are analyzed. We also provide some guidance for the selection of these methods in different real -world applications. Finally, we briefly discuss the future directions of arbitrary -scale super -resolution, which shows some inspirations for the progress of subsequent works on arbitrary -scale image and video super -resolution tasks. The repository of this work is available at https://github.com/Weepingchestnut/Arbitrary-Scale-SR.