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NTIRE 2019 Challenge on Video Deblurring: Methods and Results

Cited 35 time in Web of Science Cited 27 time in Scopus
Authors

Nah, Seungjun; Timofte, Radu; Baik, Sungyong; Hong, Seokil; Moon, Gyeongsik; Son, Sanghyun; Lee, Kyoung Mu; Wang, Xintao; Chan, Kelvin C. K.; Yu, Ke; Dong, Chao; Loy, Chen Change; Fan, Yuchen; Yu, Jiahui; Liu, Ding; Huang, Thomas S.; Sim, Hyeonjun; Kim, Munchurl; Park, Dongwon; Kim, Jisoo; Chun, Se Young; Haris, Muhammad; Shakhnarovich, Greg; Ukita, Norimichi; Zamir, Syed Waqas; Arora, Aditya; Khan, Salman; Khan, Fahad Shahbaz; Shao, Ling; Gupta, Rahul Kumar; Chudasama, Vishal; Patel, Heena; Upla, Kishor; Fan, Hongfei; Li, Guo; Zhang, Yumei; Li, Xiang; Zhang, Wenjie; He, Qingwen; Purohit, Kuldeep; Rajagopalan, A. N.; Kim, Jeonghun; Tofighi, Mohammad; Guo, Tiantong; Monga, Vishal

Issue Date
2019-06
Publisher
IEEE
Citation
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2019), pp.1974-1984
Abstract
This paper reviews the first NTIRE challenge on video deblurring (restoration of rich details and high frequency components from blurred video frames) with focus on the proposed solutions and results. A new REalistic and Diverse Scenes dataset (REDS) was employed. The challenge was divided into 2 tracks. Track 1 employed dynamic motion blurs while Track 2 had additional MPEG video compression artifacts. Each competition had 109 and 93 registered participants. Total 13 teams competed in the final testing phase. They gauge the state-of-the-art in video de blurring problem.
ISSN
2160-7508
URI
https://hdl.handle.net/10371/186439
DOI
https://doi.org/10.1109/CVPRW.2019.00249
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