NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

Yawei Li; Kai Zhang; Radu Timofte; Luc Van Gool; Fangyuan Kong; Mingxi Li; Songwei Liu; Zongcai Du; Ding Liu; Chenhui Zhou; Jingyi Chen; Qingrui Han; Zheyuan Li; Yingqi Liu; Xiangyu Chen; Haoming Cai; Yu Qiao; Chao Dong; Long Sun; Jinshan Pan; Yi Zhu; Zhikai Zong; Xiaoxiao Liu; Zheng Hui; Tao Yang; Peiran Ren; Xuansong Xie; Xian Sheng Hua; Yanbo Wang; Xiaozhong Ji; Chuming Lin; Donghao Luo; Ying Tai; Chengjie Wang; Zhizhong Zhang; Yuan Xie; Shen Cheng; Ziwei Luo; Lei Yu; Zhihong Wen; Qi Wul; Youwei Li; Haoqiang Fan; Jian Sun; Shuaicheng Liu; Yuanfei Huang; Meiguang Jin; Hua Huang; Jing Liu; Xinjian Zhang; Yan Wang; Lingshun Long; Lingshun Kong; Gen Li; Yuanfan Zhang; Zuowei Cao; Lei Sun; Panaetov Alexander; Yucong Wang; Shuhao Zhang; Yuhao Zhang; Minjie Cai; Li Wang; Lu Tian; Zheyuan Wang; Hongbing Ma; Jie Liu; Chao Chen; Yidong Cai; Jie Tang; Gangshan Wu; Weiran Wang; Shirui Huang; Honglei Lu; Huan Liu; Keyan Wang; Jun Chen; Shi Chen; Yuchun Miao; Zimo Huang; Lefei Zhang; Mustafa Ayazoglu; Wei Xiong; Chengyi Xiong; Fei Wang; Hao Li; Ruimian Wen; Zhijing Yang; Wenbin Zou; Weixin Zheng; Tian Ye; Yuncheng Zhang; Xiangzhen Kong; Aditya Arora; Syed Waqas Zamir; Salman Khan; Munawar Hayat; Fahad Shahbaz Khan; Dandan Gao; Dengwen Zhou; Qian Ning; Jingzhu Tang; Han Huang; Yufei Wang; Zhangheng Peng; Haobo Li; Wenxue Guan; Shenghua Gong; Xin Li; Jun Liu; Wanjun Wang; Dengwen Zhou; Kun Zeng; Hanjiang Lin; Xinyu Chen; Jinsheng Fang; Abhishek Kumar Sinha; S. Manthira Moorthi; Debajyoti Dhar; Hao Hsiang Yang; Zhi Kai Huang; Wei Ting Chen; Hua En Chang; Sy Yen Kuo; Wei Tan; Hao Chen; Pratik Narang; Liu Yinghua; Zhang Tianlin; Zhang Xiaoming; Dingxuan Meng; Chunwei Tian; Mashrur M. Morshed; Ahmad Omar Ahsan

doi:10.1109/CVPRW56347.2022.00118

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

Yawei Li, Kai Zhang, Radu Timofte, Luc Van Gool, Fangyuan Kong, Mingxi Li, Songwei Liu, Zongcai Du, Ding Liu, Chenhui Zhou, Jingyi Chen, Qingrui Han, Zheyuan Li, Yingqi Liu, Xiangyu Chen, Haoming Cai, Yu Qiao, Chao Dong, Long Sun, Jinshan PanYi Zhu, Zhikai Zong, Xiaoxiao Liu, Zheng Hui, Tao Yang, Peiran Ren, Xuansong Xie, Xian Sheng Hua, Yanbo Wang, Xiaozhong Ji, Chuming Lin, Donghao Luo, Ying Tai, Chengjie Wang, Zhizhong Zhang, Yuan Xie, Shen Cheng, Ziwei Luo, Lei Yu, Zhihong Wen, Qi Wul, Youwei Li, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Yuanfei Huang, Meiguang Jin, Hua Huang, Jing Liu, Xinjian Zhang, Yan Wang, Lingshun Long, Lingshun Kong, Gen Li, Yuanfan Zhang, Zuowei Cao, Lei Sun, Panaetov Alexander, Yucong Wang, Shuhao Zhang, Yuhao Zhang, Minjie Cai, Li Wang, Lu Tian, Zheyuan Wang, Hongbing Ma, Jie Liu, Chao Chen, Yidong Cai, Jie Tang, Gangshan Wu, Weiran Wang, Shirui Huang, Honglei Lu, Huan Liu, Keyan Wang, Jun Chen, Shi Chen, Yuchun Miao, Zimo Huang, Lefei Zhang, Mustafa Ayazoglu, Wei Xiong, Chengyi Xiong, Fei Wang, Hao Li, Ruimian Wen, Zhijing Yang, Wenbin Zou, Weixin Zheng, Tian Ye, Yuncheng Zhang, Xiangzhen Kong, Aditya Arora, Syed Waqas Zamir, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Dandan Gao, Dengwen Zhou, Qian Ning, Jingzhu Tang, Han Huang, Yufei Wang, Zhangheng Peng, Haobo Li, Wenxue Guan, Shenghua Gong, Xin Li, Jun Liu, Wanjun Wang, Dengwen Zhou, Kun Zeng, Hanjiang Lin, Xinyu Chen, Jinsheng Fang, Abhishek Kumar Sinha, S. Manthira Moorthi, Debajyoti Dhar, Hao Hsiang Yang, Zhi Kai Huang, Wei Ting Chen, Hua En Chang, Sy Yen Kuo, Wei Tan, Hao Chen, Pratik Narang, Liu Yinghua, Zhang Tianlin, Zhang Xiaoming, Dingxuan Meng, Chunwei Tian, Mashrur M. Morshed, Ahmad Omar Ahsan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

86 Scopus citations

Abstract

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution.

Original language	English
Title of host publication	Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
Publisher	IEEE Computer Society
Pages	1061-1101
Number of pages	41
ISBN (Electronic)	9781665487399
DOIs	https://doi.org/10.1109/CVPRW56347.2022.00118
State	Published - 2022
Externally published	Yes
Event	2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 - New Orleans, United States Duration: 19 Jun 2022 → 24 Jun 2022

Publication series

Name	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume	2022-June
ISSN (Print)	2160-7508
ISSN (Electronic)	2160-7516

Conference

Conference	2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
Country/Territory	United States
City	New Orleans
Period	19/06/22 → 24/06/22

Access to Document

10.1109/CVPRW56347.2022.00118

Cite this

Li, Y., Zhang, K., Timofte, R., Van Gool, L., Kong, F., Li, M., Liu, S., Du, Z., Liu, D., Zhou, C., Chen, J., Han, Q., Li, Z., Liu, Y., Chen, X., Cai, H., Qiao, Y., Dong, C., Sun, L., ... Ahsan, A. O. (2022). NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results. In Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 (pp. 1061-1101). (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2022-June). IEEE Computer Society. https://doi.org/10.1109/CVPRW56347.2022.00118

@inproceedings{17e0c021cd834e3fb51729144ee70abd,

title = "NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results",

abstract = "This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution.",

author = "Yawei Li and Kai Zhang and Radu Timofte and \{Van Gool\}, Luc and Fangyuan Kong and Mingxi Li and Songwei Liu and Zongcai Du and Ding Liu and Chenhui Zhou and Jingyi Chen and Qingrui Han and Zheyuan Li and Yingqi Liu and Xiangyu Chen and Haoming Cai and Yu Qiao and Chao Dong and Long Sun and Jinshan Pan and Yi Zhu and Zhikai Zong and Xiaoxiao Liu and Zheng Hui and Tao Yang and Peiran Ren and Xuansong Xie and Hua, \{Xian Sheng\} and Yanbo Wang and Xiaozhong Ji and Chuming Lin and Donghao Luo and Ying Tai and Chengjie Wang and Zhizhong Zhang and Yuan Xie and Shen Cheng and Ziwei Luo and Lei Yu and Zhihong Wen and Qi Wul and Youwei Li and Haoqiang Fan and Jian Sun and Shuaicheng Liu and Yuanfei Huang and Meiguang Jin and Hua Huang and Jing Liu and Xinjian Zhang and Yan Wang and Lingshun Long and Lingshun Kong and Gen Li and Yuanfan Zhang and Zuowei Cao and Lei Sun and Panaetov Alexander and Yucong Wang and Shuhao Zhang and Yuhao Zhang and Minjie Cai and Li Wang and Lu Tian and Zheyuan Wang and Hongbing Ma and Jie Liu and Chao Chen and Yidong Cai and Jie Tang and Gangshan Wu and Weiran Wang and Shirui Huang and Honglei Lu and Huan Liu and Keyan Wang and Jun Chen and Shi Chen and Yuchun Miao and Zimo Huang and Lefei Zhang and Mustafa Ayazoglu and Wei Xiong and Chengyi Xiong and Fei Wang and Hao Li and Ruimian Wen and Zhijing Yang and Wenbin Zou and Weixin Zheng and Tian Ye and Yuncheng Zhang and Xiangzhen Kong and Aditya Arora and Zamir, \{Syed Waqas\} and Salman Khan and Munawar Hayat and Khan, \{Fahad Shahbaz\} and Dandan Gao and Dengwen Zhou and Qian Ning and Jingzhu Tang and Han Huang and Yufei Wang and Zhangheng Peng and Haobo Li and Wenxue Guan and Shenghua Gong and Xin Li and Jun Liu and Wanjun Wang and Dengwen Zhou and Kun Zeng and Hanjiang Lin and Xinyu Chen and Jinsheng Fang and Sinha, \{Abhishek Kumar\} and Moorthi, \{S. Manthira\} and Debajyoti Dhar and Yang, \{Hao Hsiang\} and Huang, \{Zhi Kai\} and Chen, \{Wei Ting\} and Chang, \{Hua En\} and Kuo, \{Sy Yen\} and Wei Tan and Hao Chen and Pratik Narang and Liu Yinghua and Zhang Tianlin and Zhang Xiaoming and Dingxuan Meng and Chunwei Tian and Morshed, \{Mashrur M.\} and Ahsan, \{Ahmad Omar\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 ; Conference date: 19-06-2022 Through 24-06-2022",

year = "2022",

doi = "10.1109/CVPRW56347.2022.00118",

language = "英语",

series = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",

publisher = "IEEE Computer Society",

pages = "1061--1101",

booktitle = "Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022",

address = "美国",

}

Li, Y, Zhang, K, Timofte, R, Van Gool, L, Kong, F, Li, M, Liu, S, Du, Z, Liu, D, Zhou, C, Chen, J, Han, Q, Li, Z, Liu, Y, Chen, X, Cai, H, Qiao, Y, Dong, C, Sun, L, Pan, J, Zhu, Y, Zong, Z, Liu, X, Hui, Z, Yang, T, Ren, P, Xie, X, Hua, XS, Wang, Y, Ji, X, Lin, C, Luo, D, Tai, Y, Wang, C, Zhang, Z , Xie, Y, Cheng, S, Luo, Z, Yu, L, Wen, Z, Wul, Q, Li, Y, Fan, H, Sun, J, Liu, S, Huang, Y, Jin, M, Huang, H, Liu, J, Zhang, X, Wang, Y, Long, L, Kong, L, Li, G, Zhang, Y, Cao, Z, Sun, L, Alexander, P, Wang, Y, Zhang, S, Zhang, Y, Cai, M, Wang, L, Tian, L, Wang, Z, Ma, H, Liu, J, Chen, C, Cai, Y, Tang, J, Wu, G, Wang, W, Huang, S, Lu, H, Liu, H, Wang, K, Chen, J, Chen, S, Miao, Y, Huang, Z, Zhang, L, Ayazoglu, M, Xiong, W, Xiong, C, Wang, F, Li, H, Wen, R, Yang, Z, Zou, W, Zheng, W, Ye, T, Zhang, Y, Kong, X, Arora, A, Zamir, SW, Khan, S, Hayat, M, Khan, FS, Gao, D, Zhou, D, Ning, Q, Tang, J, Huang, H, Wang, Y, Peng, Z, Li, H, Guan, W, Gong, S, Li, X, Liu, J, Wang, W, Zhou, D, Zeng, K, Lin, H, Chen, X, Fang, J, Sinha, AK, Moorthi, SM, Dhar, D, Yang, HH, Huang, ZK, Chen, WT, Chang, HE, Kuo, SY, Tan, W, Chen, H, Narang, P, Yinghua, L, Tianlin, Z, Xiaoming, Z, Meng, D, Tian, C, Morshed, MM & Ahsan, AO 2022, NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results. in Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2022-June, IEEE Computer Society, pp. 1061-1101, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022, New Orleans, United States, 19/06/22. https://doi.org/10.1109/CVPRW56347.2022.00118

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results. / Li, Yawei; Zhang, Kai; Timofte, Radu et al.
Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022. IEEE Computer Society, 2022. p. 1061-1101 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2022-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - NTIRE 2022 Challenge on Efficient Super-Resolution

T2 - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022

AU - Li, Yawei

AU - Zhang, Kai

AU - Timofte, Radu

AU - Van Gool, Luc

AU - Kong, Fangyuan

AU - Li, Mingxi

AU - Liu, Songwei

AU - Du, Zongcai

AU - Liu, Ding

AU - Zhou, Chenhui

AU - Chen, Jingyi

AU - Han, Qingrui

AU - Li, Zheyuan

AU - Liu, Yingqi

AU - Chen, Xiangyu

AU - Cai, Haoming

AU - Qiao, Yu

AU - Dong, Chao

AU - Sun, Long

AU - Pan, Jinshan

AU - Zhu, Yi

AU - Zong, Zhikai

AU - Liu, Xiaoxiao

AU - Hui, Zheng

AU - Yang, Tao

AU - Ren, Peiran

AU - Xie, Xuansong

AU - Hua, Xian Sheng

AU - Wang, Yanbo

AU - Ji, Xiaozhong

AU - Lin, Chuming

AU - Luo, Donghao

AU - Tai, Ying

AU - Wang, Chengjie

AU - Zhang, Zhizhong

AU - Xie, Yuan

AU - Cheng, Shen

AU - Luo, Ziwei

AU - Yu, Lei

AU - Wen, Zhihong

AU - Wul, Qi

AU - Li, Youwei

AU - Fan, Haoqiang

AU - Sun, Jian

AU - Liu, Shuaicheng

AU - Huang, Yuanfei

AU - Jin, Meiguang

AU - Huang, Hua

AU - Liu, Jing

AU - Zhang, Xinjian

AU - Wang, Yan

AU - Long, Lingshun

AU - Kong, Lingshun

AU - Li, Gen

AU - Zhang, Yuanfan

AU - Cao, Zuowei

AU - Sun, Lei

AU - Alexander, Panaetov

AU - Wang, Yucong

AU - Zhang, Shuhao

AU - Zhang, Yuhao

AU - Cai, Minjie

AU - Wang, Li

AU - Tian, Lu

AU - Wang, Zheyuan

AU - Ma, Hongbing

AU - Liu, Jie

AU - Chen, Chao

AU - Cai, Yidong

AU - Tang, Jie

AU - Wu, Gangshan

AU - Wang, Weiran

AU - Huang, Shirui

AU - Lu, Honglei

AU - Liu, Huan

AU - Wang, Keyan

AU - Chen, Jun

AU - Chen, Shi

AU - Miao, Yuchun

AU - Huang, Zimo

AU - Zhang, Lefei

AU - Ayazoglu, Mustafa

AU - Xiong, Wei

AU - Xiong, Chengyi

AU - Wang, Fei

AU - Li, Hao

AU - Wen, Ruimian

AU - Yang, Zhijing

AU - Zou, Wenbin

AU - Zheng, Weixin

AU - Ye, Tian

AU - Zhang, Yuncheng

AU - Kong, Xiangzhen

AU - Arora, Aditya

AU - Zamir, Syed Waqas

AU - Khan, Salman

AU - Hayat, Munawar

AU - Khan, Fahad Shahbaz

AU - Gao, Dandan

AU - Zhou, Dengwen

AU - Ning, Qian

AU - Tang, Jingzhu

AU - Huang, Han

AU - Wang, Yufei

AU - Peng, Zhangheng

AU - Li, Haobo

AU - Guan, Wenxue

AU - Gong, Shenghua

AU - Li, Xin

AU - Liu, Jun

AU - Wang, Wanjun

AU - Zhou, Dengwen

AU - Zeng, Kun

AU - Lin, Hanjiang

AU - Chen, Xinyu

AU - Fang, Jinsheng

AU - Sinha, Abhishek Kumar

AU - Moorthi, S. Manthira

AU - Dhar, Debajyoti

AU - Yang, Hao Hsiang

AU - Huang, Zhi Kai

AU - Chen, Wei Ting

AU - Chang, Hua En

AU - Kuo, Sy Yen

AU - Tan, Wei

AU - Chen, Hao

AU - Narang, Pratik

AU - Yinghua, Liu

AU - Tianlin, Zhang

AU - Xiaoming, Zhang

AU - Meng, Dingxuan

AU - Tian, Chunwei

AU - Morshed, Mashrur M.

AU - Ahsan, Ahmad Omar

PY - 2022

Y1 - 2022

N2 - This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution.

AB - This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution.

UR - https://www.scopus.com/pages/publications/85129009024

U2 - 10.1109/CVPRW56347.2022.00118

DO - 10.1109/CVPRW56347.2022.00118

M3 - 会议稿件

AN - SCOPUS:85129009024

T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

SP - 1061

EP - 1101

BT - Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022

PB - IEEE Computer Society

Y2 - 19 June 2022 through 24 June 2022

ER -

Li Y, Zhang K, Timofte R, Van Gool L, Kong F, Li M et al. NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results. In Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022. IEEE Computer Society. 2022. p. 1061-1101. (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops). doi: 10.1109/CVPRW56347.2022.00118

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

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