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NTIRE 2020 challenge on image and video deblurring
Date Issued
01-06-2020
Author(s)
Nah, Seungjun
Son, Sanghyun
Timofte, Radu
Lee, Kyoung Mu
Tseng, Yu
Xu, Yu Syuan
Chiang, Cheng Ming
Tsai, Yi Min
Brehm, Stephan
Scherer, Sebastian
Xu, Dejia
Chu, Yihao
Sun, Qingyan
Jiang, Jiaqin
Duan, Lunhao
Yao, Jian
Purpohit, Kuldeep
Suin, Maitreya
Indian Institute of Technology, Madras
Ito, Yuichi
Hrishikesh, H. P.S.
Puthussery, Densen
Akhil, A. K.
Jiji, V. C.
Kim, Guisik
Deepa, P. L.
Xiong, Zhiwei
Huang, Jie
Liu, Dong
Kim, Sangmin
Nam, Hyungjoon
Kim, Jisu
Jeong, Jechang
Huang, Shihua
Fan, Yuchen
Yu, Jiahui
Yu, Haichao
Huang, Thomas S.
Zhou, Ya
Li, Xin
Liu, Sen
Chen, Zhibo
Dutta, Saikat
Das, Sourya Dipta
Garg, Shivam
Sprague, Daniel
Patel, Bhrij
Huck, Thomas
Abstract
Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this challenge, we present the evaluation results from 3 competition tracks as well as the proposed solutions. Track 1 aims to develop single-image deblurring methods focusing on restoration quality. On Track 2, the image deblurring methods are executed on a mobile platform to find the balance of the running speed and the restoration accuracy. Track 3 targets developing video deblurring methods that exploit the temporal relation between input frames. In each competition, there were 163, 135, and 102 registered participants and in the final testing phase, 9, 4, and 7 teams competed. The winning methods demonstrate the state-of-the-art performance on image and video deblurring tasks.
Volume
2020-June