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 |
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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 | |
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) |
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Volume | 12370 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 16th European Conference on Computer Vision, ECCV 2020 |
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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