Video Enhancement Based on Unpaired Learning

Abstract

Distortion of real video is affected by many factors. Many existing old videos n have the problem of low definition. However, most video enhancement based on paired learning is trained for specific degradation problem, lack of the ability to enhance real low-definition video with unknown distortion. In this paper, we propose a joint video enhancement algorithm based on unpaired learning, which uses a high-definition video to enhance a low-definition video with similar contents. In order to train the network, we also build three pairs of unpaired video datasets with chimpanzee, city night view and military figures as contents. In experiments, we compare our method with enhancement algorithm based on paired learning and other unpaired framework and find that our method achieves a higher performance,

Publication
2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)
Jinjin Chen
Jinjin Chen
M.S. Degree

As a master student at SJTU Media Lab, my research is mainly about video enhancement with aid of compressed information, under the direction of Prof. Li Song.

Hengsheng Zhang
Hengsheng Zhang
PhD Student

I’m a Research PHD candidate at SJTU Media Lab. I’m doing my research on Low-level Vision Problem and Video Enhancement, under the direction of Prof. Li Song.

Li Song
Li Song
Professor, IEEE Senior Member

Professor, Doctoral Supervisor, the Deputy Director of the Institute of Image Communication and Network Engineering of Shanghai Jiao Tong University, the Double-Appointed Professor of the Institute of Artificial Intelligence and the Collaborative Innovation Center of Future Media Network, the Deputy Secretary-General of the China Video User Experience Alliance and head of the standards group.