Global and Local Enhancement Networks for Paired and Unpaired Image Enhancement

Han Ul Kim; Young Jun Koh; Chang Su Kim

doi:10.1007/978-3-030-58595-2_21

Global and Local Enhancement Networks for Paired and Unpaired Image Enhancement

Han Ul Kim, Young Jun Koh, Chang Su Kim

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

53 Citations (Scopus)

Abstract

A novel approach for paired and unpaired image enhancement is proposed in this work. First, we develop global enhancement network (GEN) and local enhancement network (LEN), which can faithfully enhance images. The proposed GEN performs the channel-wise intensity transforms that can be trained easier than the pixel-wise prediction. The proposed LEN refines GEN results based on spatial filtering. Second, we propose different training schemes for paired learning and unpaired learning to train GEN and LEN. Especially, we propose a two-stage training scheme based on generative adversarial networks for unpaired learning. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-arts in paired and unpaired image enhancement. Notably, the proposed unpaired image enhancement algorithm provides better results than recent state-of-the-art paired image enhancement algorithms. The source codes and trained models are available at https://github.com/hukim1124/GleNet.

Original language	English
Title of host publication	Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings
Editors	Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	339-354
Number of pages	16
ISBN (Print)	9783030585945
DOIs	https://doi.org/10.1007/978-3-030-58595-2_21
Publication status	Published - 2020
Event	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	12370 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	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.

Keywords

Generative adversarial network
Image enhancement
Unpaired learning

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-58595-2_21

Cite this

Kim, H. U., Koh, Y. J., & Kim, C. S. (2020). Global and Local Enhancement Networks for Paired and Unpaired Image Enhancement. In A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Eds.), Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings (pp. 339-354). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12370 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58595-2_21

Global and Local Enhancement Networks for Paired and Unpaired Image Enhancement. / Kim, Han Ul; Koh, Young Jun; Kim, Chang Su.
Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings. ed. / Andrea Vedaldi; Horst Bischof; Thomas Brox; Jan-Michael Frahm. Springer Science and Business Media Deutschland GmbH, 2020. p. 339-354 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12370 LNCS).

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

Kim, HU, Koh, YJ & Kim, CS 2020, Global and Local Enhancement Networks for Paired and Unpaired Image Enhancement. in A Vedaldi, H Bischof, T Brox & J-M Frahm (eds), Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12370 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 339-354, 16th European Conference on Computer Vision, ECCV 2020, Glasgow, United Kingdom, 20/8/23. https://doi.org/10.1007/978-3-030-58595-2_21

Kim HU, Koh YJ, Kim CS. Global and Local Enhancement Networks for Paired and Unpaired Image Enhancement. In Vedaldi A, Bischof H, Brox T, Frahm JM, editors, Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 339-354. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-58595-2_21

Kim, Han Ul ; Koh, Young Jun ; Kim, Chang Su. / Global and Local Enhancement Networks for Paired and Unpaired Image Enhancement. Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings. editor / Andrea Vedaldi ; Horst Bischof ; Thomas Brox ; Jan-Michael Frahm. Springer Science and Business Media Deutschland GmbH, 2020. pp. 339-354 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "A novel approach for paired and unpaired image enhancement is proposed in this work. First, we develop global enhancement network (GEN) and local enhancement network (LEN), which can faithfully enhance images. The proposed GEN performs the channel-wise intensity transforms that can be trained easier than the pixel-wise prediction. The proposed LEN refines GEN results based on spatial filtering. Second, we propose different training schemes for paired learning and unpaired learning to train GEN and LEN. Especially, we propose a two-stage training scheme based on generative adversarial networks for unpaired learning. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-arts in paired and unpaired image enhancement. Notably, the proposed unpaired image enhancement algorithm provides better results than recent state-of-the-art paired image enhancement algorithms. The source codes and trained models are available at https://github.com/hukim1124/GleNet.",

keywords = "Generative adversarial network, Image enhancement, Unpaired learning",

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AB - A novel approach for paired and unpaired image enhancement is proposed in this work. First, we develop global enhancement network (GEN) and local enhancement network (LEN), which can faithfully enhance images. The proposed GEN performs the channel-wise intensity transforms that can be trained easier than the pixel-wise prediction. The proposed LEN refines GEN results based on spatial filtering. Second, we propose different training schemes for paired learning and unpaired learning to train GEN and LEN. Especially, we propose a two-stage training scheme based on generative adversarial networks for unpaired learning. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-arts in paired and unpaired image enhancement. Notably, the proposed unpaired image enhancement algorithm provides better results than recent state-of-the-art paired image enhancement algorithms. The source codes and trained models are available at https://github.com/hukim1124/GleNet.

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Global and Local Enhancement Networks for Paired and Unpaired Image Enhancement

Abstract

Publication series

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Keywords

ASJC Scopus subject areas

Access to Document

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