TY - GEN
T1 - NTIRE 2021 challenge on image deblurring
AU - NTIRE 2021 team
AU - Nah, Seungjun
AU - Son, Sanghyun
AU - Lee, Suyoung
AU - Timofte, Radu
AU - Lee, Kyoung Mu
AU - Xiong, Zhiwei
AU - Xu, Ruikang
AU - Xiao, Zeyu
AU - Huang, Jie
AU - Zhang, Yueyi
AU - Chen, Liangyu
AU - Zhang, Jie
AU - Lu, Xin
AU - Chu, Xiaojie
AU - Chen, Chengpeng
AU - Xi, Si
AU - Wei, Jia
AU - Bai, Haoran
AU - Cheng, Songsheng
AU - Wei, Hao
AU - Sun, Long
AU - Tang, Jinhui
AU - Pan, Jinshan
AU - Lee, Donghyeon
AU - Lee, Chulhee
AU - Kim, Taesung
AU - Wang, Xiaobing
AU - Zhangr, Dafeng
AU - Pan, Zhihong
AU - Lin, Tianwei
AU - Wu, Wenhao
AU - He, Dongliang
AU - Li, Baopu
AU - Li, Boyun
AU - Xi, Teng
AU - Zhang, Gang
AU - Liu, Jingtuo
AU - Han, Junyu
AU - Ding, Errui
AU - Tao, Guangping
AU - Chu, Wenqing
AU - Cao, Yun
AU - Luo, Donghao
AU - Tai, Ying
AU - Lu, Tong
AU - Wang, Chengjie
AU - Li, Jilin
AU - Huang, Feiyue
AU - Chen, Hanting
AU - Jeon, Byeungwoo
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on Image Deblurring. In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions. While both the tracks aim to recover a high-quality clean image from a blurry image, different artifacts are jointly involved. In track 1, the blurry images are in a low resolution while track 2 images are compressed in JPEG format. In each competition, there were 338 and 238 registered participants and in the final testing phase, 18 and 17 teams competed. The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts.
AB - Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on Image Deblurring. In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions. While both the tracks aim to recover a high-quality clean image from a blurry image, different artifacts are jointly involved. In track 1, the blurry images are in a low resolution while track 2 images are compressed in JPEG format. In each competition, there were 338 and 238 registered participants and in the final testing phase, 18 and 17 teams competed. The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts.
UR - https://www.scopus.com/pages/publications/85116001680
U2 - 10.1109/CVPRW53098.2021.00025
DO - 10.1109/CVPRW53098.2021.00025
M3 - Conference contribution
AN - SCOPUS:85116001680
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 149
EP - 165
BT - Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
PB - IEEE Computer Society
T2 - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
Y2 - 19 June 2021 through 25 June 2021
ER -