Survey on Video Super-resolution Based on Deep Learning

Abstract

Video super-resolution (VSR) aims to reconstruct a high-resolution video from its corresponding low-resolution version.Recently,VSR has made great progress driven by deep learning.In order to further promote VSR,this survey makes a comprehensive summary of VSR,and makes a taxonomy,analysis and comparison of existing algorithms.Firstly,since different frameworks are very important for VSR,we group the VSR approaches into two categories according to different frameworks:iterative- and recurrent-network based VSR approaches.The advantages and disadvantages of different networks are further compared and analyzed.Secondly,we comprehensively introduce the VSR datasets,summarize existing algorithms and further compare these algorithms on some benchmark datasets.Finally,the key challenges and the application of VSR methods are analyzed and prospected.

Keywords

• video super-resolution|deep learning|convolutional neural network|inter-frame information