Video Super Resolution Based on Deep Learning: A comprehensive survey
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In recent years, deep learning has made great progress in the fields of image recognition, video analysis, natural language processing and speech recognition, including video super-resolution tasks. In this survey, we comprehensively investigate 28 state-of-the-art video super-resolution methods based on deep learning. It is well known that the leverage of information within video frames is important for video super-resolution. Hence we propose a taxonomy and classify the methods into six sub-categories according to the ways of utilizing inter-frame information. Moreover, the architectures and implementation details (including input and output, loss function and learning rate) of all the methods are depicted in details. Finally, we summarize and compare their performance on some benchmark datasets under different magnification factors. We also discuss some challenges, which need to be further addressed by researchers in the community of video super-resolution. Therefore, this work is expected to make a contribution to the future development of research in video super-resolution, and alleviate understandability and transferability of existing and future techniques into practice.
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