AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

Kai Zhang; Martin Danelljan; Yawei Li; Radu Timofte; Jie Liu; Jie Tang; Gangshan Wu; Yu Zhu; Xiangyu He; Wenjie Xu; Chenghua Li; Cong Leng; Jian Cheng; Guangyang Wu; Wenyi Wang; Xiaohong Liu; Hengyuan Zhao; Xiangtao Kong; Jingwen He; Yu Qiao; Chao Dong; Xiaotong Luo; Liang Chen; Jiangtao Zhang; Maitreya Suin; Kuldeep Purohit; A. N. Rajagopalan; Xiaochuan Li; Zhiqiang Lang; Jiangtao Nie; Wei Wei; Lei Zhang; Abdul Muqeet; Jiwon Hwang; Subin Yang; Jung Heum Kang; Sung Ho Bae; Yongwoo Kim; Yanyun Qu; Geun Woo Jeon; Jun Ho Choi; Jun Hyuk Kim; Jong Seok Lee; Steven Marty; Eric Marty; Dongliang Xiong; Siang Chen; Lin Zha; Jiande Jiang; Xinbo Gao; Wen Lu; Haicheng Wang; Vineeth Bhaskara; Alex Levinshtein; Stavros Tsogkas; Allan Jepson; Xiangzhen Kong; Tongtong Zhao; Shanshan Zhao; P. S. Hrishikesh; Densen Puthussery; C. V. Jiji; Nan Nan; Shuai Liu; Jie Cai; Zibo Meng; Jiaming Ding; Chiu Man Ho; Xuehui Wang; Qiong Yan; Yuzhi Zhao; Long Chen; Long Sun; Wenhao Wang; Zhenbing Liu; Rushi Lan; Rao Muhammad Umer; Christian Micheloni

doi:10.1007/978-3-030-67070-2_1

AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

Kai Zhang, Martin Danelljan, Yawei Li, Radu Timofte, Jie Liu, Jie Tang, Gangshan Wu, Yu Zhu, Xiangyu He, Wenjie Xu, Chenghua Li, Cong Leng, Jian Cheng, Guangyang Wu, Wenyi Wang, Xiaohong Liu, Hengyuan Zhao, Xiangtao Kong, Jingwen He, Yu QiaoChao Dong, Xiaotong Luo, Liang Chen, Jiangtao Zhang, Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan, Xiaochuan Li, Zhiqiang Lang, Jiangtao Nie, Wei Wei, Lei Zhang, Abdul Muqeet, Jiwon Hwang, Subin Yang, Jung Heum Kang, Sung Ho Bae, Yongwoo Kim, Yanyun Qu, Geun Woo Jeon, Jun Ho Choi, Jun Hyuk Kim, Jong Seok Lee, Steven Marty, Eric Marty, Dongliang Xiong, Siang Chen, Lin Zha, Jiande Jiang, Xinbo Gao, Wen Lu, Haicheng Wang, Vineeth Bhaskara, Alex Levinshtein, Stavros Tsogkas, Allan Jepson, Xiangzhen Kong, Tongtong Zhao, Shanshan Zhao, P. S. Hrishikesh, Densen Puthussery, C. V. Jiji, Nan Nan, Shuai Liu, Jie Cai, Zibo Meng, Jiaming Ding, Chiu Man Ho, Xuehui Wang, Qiong Yan, Yuzhi Zhao, Long Chen, Long Sun, Wenhao Wang, Zhenbing Liu, Rushi Lan, Rao Muhammad Umer, Christian Micheloni

School of Integrated Technology

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

71 Citations (Scopus)

Abstract

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor × 4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.

Original language	English
Title of host publication	Computer Vision – ECCV 2020 Workshops, Proceedings
Editors	Adrien Bartoli, Andrea Fusiello
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	5-40
Number of pages	36
ISBN (Print)	9783030670696
DOIs	https://doi.org/10.1007/978-3-030-67070-2_1
Publication status	Published - 2020
Event	Workshops held at the 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom Duration: 2020 Aug 23 → 2020 Aug 28

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12537 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Workshops held at the 16th European Conference on Computer Vision, ECCV 2020
Country/Territory	United Kingdom
City	Glasgow
Period	20/8/23 → 20/8/28

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-67070-2_1

Cite this

Zhang, K., Danelljan, M., Li, Y., Timofte, R., Liu, J., Tang, J., Wu, G., Zhu, Y., He, X., Xu, W., Li, C., Leng, C., Cheng, J., Wu, G., Wang, W., Liu, X., Zhao, H., Kong, X., He, J., ... Micheloni, C. (2020). AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results. In A. Bartoli, & A. Fusiello (Eds.), Computer Vision – ECCV 2020 Workshops, Proceedings (pp. 5-40). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12537 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-67070-2_1

Zhang, Kai ; Danelljan, Martin ; Li, Yawei et al. / AIM 2020 Challenge on Efficient Super-Resolution : Methods and Results. Computer Vision – ECCV 2020 Workshops, Proceedings. editor / Adrien Bartoli ; Andrea Fusiello. Springer Science and Business Media Deutschland GmbH, 2020. pp. 5-40 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{8ca5eb44e434403a82904718d405793f,

title = "AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results",

abstract = "This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor × 4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.",

author = "Kai Zhang and Martin Danelljan and Yawei Li and Radu Timofte and Jie Liu and Jie Tang and Gangshan Wu and Yu Zhu and Xiangyu He and Wenjie Xu and Chenghua Li and Cong Leng and Jian Cheng and Guangyang Wu and Wenyi Wang and Xiaohong Liu and Hengyuan Zhao and Xiangtao Kong and Jingwen He and Yu Qiao and Chao Dong and Xiaotong Luo and Liang Chen and Jiangtao Zhang and Maitreya Suin and Kuldeep Purohit and Rajagopalan, {A. N.} and Xiaochuan Li and Zhiqiang Lang and Jiangtao Nie and Wei Wei and Lei Zhang and Abdul Muqeet and Jiwon Hwang and Subin Yang and Kang, {Jung Heum} and Bae, {Sung Ho} and Yongwoo Kim and Yanyun Qu and Jeon, {Geun Woo} and Choi, {Jun Ho} and Kim, {Jun Hyuk} and Lee, {Jong Seok} and Steven Marty and Eric Marty and Dongliang Xiong and Siang Chen and Lin Zha and Jiande Jiang and Xinbo Gao and Wen Lu and Haicheng Wang and Vineeth Bhaskara and Alex Levinshtein and Stavros Tsogkas and Allan Jepson and Xiangzhen Kong and Tongtong Zhao and Shanshan Zhao and Hrishikesh, {P. S.} and Densen Puthussery and Jiji, {C. V.} and Nan Nan and Shuai Liu and Jie Cai and Zibo Meng and Jiaming Ding and Ho, {Chiu Man} and Xuehui Wang and Qiong Yan and Yuzhi Zhao and Long Chen and Long Sun and Wenhao Wang and Zhenbing Liu and Rushi Lan and Umer, {Rao Muhammad} and Christian Micheloni",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; Workshops held at the 16th European Conference on Computer Vision, ECCV 2020 ; Conference date: 23-08-2020 Through 28-08-2020",

year = "2020",

doi = "10.1007/978-3-030-67070-2_1",

language = "English",

isbn = "9783030670696",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "5--40",

editor = "Adrien Bartoli and Andrea Fusiello",

booktitle = "Computer Vision – ECCV 2020 Workshops, Proceedings",

address = "Germany",

}

Zhang, K, Danelljan, M, Li, Y, Timofte, R, Liu, J, Tang, J, Wu, G, Zhu, Y, He, X, Xu, W, Li, C, Leng, C, Cheng, J, Wu, G, Wang, W, Liu, X, Zhao, H, Kong, X, He, J, Qiao, Y, Dong, C, Luo, X, Chen, L, Zhang, J, Suin, M, Purohit, K, Rajagopalan, AN, Li, X, Lang, Z, Nie, J, Wei, W, Zhang, L, Muqeet, A, Hwang, J, Yang, S, Kang, JH, Bae, SH, Kim, Y, Qu, Y, Jeon, GW, Choi, JH, Kim, JH, Lee, JS, Marty, S, Marty, E, Xiong, D, Chen, S, Zha, L, Jiang, J, Gao, X, Lu, W, Wang, H, Bhaskara, V, Levinshtein, A, Tsogkas, S, Jepson, A, Kong, X, Zhao, T, Zhao, S, Hrishikesh, PS, Puthussery, D, Jiji, CV, Nan, N, Liu, S, Cai, J, Meng, Z, Ding, J, Ho, CM, Wang, X, Yan, Q, Zhao, Y, Chen, L, Sun, L, Wang, W, Liu, Z, Lan, R, Umer, RM & Micheloni, C 2020, AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results. in A Bartoli & A Fusiello (eds), Computer Vision – ECCV 2020 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12537 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 5-40, Workshops held at the 16th European Conference on Computer Vision, ECCV 2020, Glasgow, United Kingdom, 20/8/23. https://doi.org/10.1007/978-3-030-67070-2_1

AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results. / Zhang, Kai; Danelljan, Martin; Li, Yawei et al.
Computer Vision – ECCV 2020 Workshops, Proceedings. ed. / Adrien Bartoli; Andrea Fusiello. Springer Science and Business Media Deutschland GmbH, 2020. p. 5-40 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12537 LNCS).

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

TY - GEN

T1 - AIM 2020 Challenge on Efficient Super-Resolution

T2 - Workshops held at the 16th European Conference on Computer Vision, ECCV 2020

AU - Zhang, Kai

AU - Danelljan, Martin

AU - Li, Yawei

AU - Timofte, Radu

AU - Liu, Jie

AU - Tang, Jie

AU - Wu, Gangshan

AU - Zhu, Yu

AU - He, Xiangyu

AU - Xu, Wenjie

AU - Li, Chenghua

AU - Leng, Cong

AU - Cheng, Jian

AU - Wu, Guangyang

AU - Wang, Wenyi

AU - Liu, Xiaohong

AU - Zhao, Hengyuan

AU - Kong, Xiangtao

AU - He, Jingwen

AU - Qiao, Yu

AU - Dong, Chao

AU - Luo, Xiaotong

AU - Chen, Liang

AU - Zhang, Jiangtao

AU - Suin, Maitreya

AU - Purohit, Kuldeep

AU - Rajagopalan, A. N.

AU - Li, Xiaochuan

AU - Lang, Zhiqiang

AU - Nie, Jiangtao

AU - Wei, Wei

AU - Zhang, Lei

AU - Muqeet, Abdul

AU - Hwang, Jiwon

AU - Yang, Subin

AU - Kang, Jung Heum

AU - Bae, Sung Ho

AU - Kim, Yongwoo

AU - Qu, Yanyun

AU - Jeon, Geun Woo

AU - Choi, Jun Ho

AU - Kim, Jun Hyuk

AU - Lee, Jong Seok

AU - Marty, Steven

AU - Marty, Eric

AU - Xiong, Dongliang

AU - Chen, Siang

AU - Zha, Lin

AU - Jiang, Jiande

AU - Gao, Xinbo

AU - Lu, Wen

AU - Wang, Haicheng

AU - Bhaskara, Vineeth

AU - Levinshtein, Alex

AU - Tsogkas, Stavros

AU - Jepson, Allan

AU - Kong, Xiangzhen

AU - Zhao, Tongtong

AU - Zhao, Shanshan

AU - Hrishikesh, P. S.

AU - Puthussery, Densen

AU - Jiji, C. V.

AU - Nan, Nan

AU - Liu, Shuai

AU - Cai, Jie

AU - Meng, Zibo

AU - Ding, Jiaming

AU - Ho, Chiu Man

AU - Wang, Xuehui

AU - Yan, Qiong

AU - Zhao, Yuzhi

AU - Chen, Long

AU - Sun, Long

AU - Wang, Wenhao

AU - Liu, Zhenbing

AU - Lan, Rushi

AU - Umer, Rao Muhammad

AU - Micheloni, Christian

PY - 2020

Y1 - 2020

N2 - This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor × 4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.

AB - This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor × 4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.

UR - http://www.scopus.com/inward/record.url?scp=85101569286&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85101569286&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-67070-2_1

DO - 10.1007/978-3-030-67070-2_1

M3 - Conference contribution

AN - SCOPUS:85101569286

SN - 9783030670696

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 5

EP - 40

BT - Computer Vision – ECCV 2020 Workshops, Proceedings

A2 - Bartoli, Adrien

A2 - Fusiello, Andrea

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 23 August 2020 through 28 August 2020

ER -

Zhang K, Danelljan M, Li Y, Timofte R, Liu J, Tang J et al. AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results. In Bartoli A, Fusiello A, editors, Computer Vision – ECCV 2020 Workshops, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 5-40. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-67070-2_1

AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

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