학술논문
NTIRE 2020 Challenge on Image and Video Deblurring
Document Type
Conference
Author
Nah, S.; Son, S.; Timofte, R.; Lee, K.M.; Tseng, Y.; Xu, Y.-S.; Chiang, C.-M.; Tsai, Y.-M.; Brehm, S.; Scherer, S.; Xu, D.; Chu, Y.; Sun, Q.; Jiang, J.; Duan, L.; Yao, J.; Purpohit, K.; Suin, M.; Rajagopalan, A.N.; Ito, Y.; Hrishikesh, P.S.; Puthussery, D.; Akhil, K.A.; Jiji, C.V.; Kim, G.; Deepa, P.L.; Xiong, Z.; Huang, J.; Liu, D.; Kim, S.; Nam, H.; Kim, J.; Jeong, J.; Huang, S.; Fan, Y.; Yu, J.; Yu, H.; Huang, T.S.; Zhou, Y.; Li, X.; Liu, S.; Chen, Z.; Dutta, S.; Das, S.D.; Garg, S.; Sprague, D.; Patel, B.; Huck, T.
Source
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Computer Vision and Pattern Recognition Workshops (CVPRW),2020 IEEE/CVF Conference on. :1662-1675 Jun, 2020
Subject
Language
ISSN
2160-7516
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.