wsg-gan-color-edition
Weak Segmentation-Guided GAN for Realistic Color Edition presented at ICIAP 2023
Science Score: 67.0%
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Repository
Weak Segmentation-Guided GAN for Realistic Color Edition presented at ICIAP 2023
Basic Info
- Host: GitHub
- Owner: VincentAuriau
- Language: TeX
- Default Branch: main
- Homepage: https://link.springer.com/chapter/10.1007/978-3-031-43148-7_41
- Size: 17.6 MB
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- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files
README.md
Weak Segmentation-Guided GAN for Realistic Color Edition
Paper presented at ICIAP 2023
Abstract
Editing the color of images in a realistic way finds many applications such as changing the perception of an image, data augmentation or film post processing. The design of an automatic tool is a complex and long addressed challenge. In particular, two properties are difficult to meet altogether: generating realistic results without artifacts and the possibility to precisely choose the future color. Conventional methods using segmentation and histogram matching maximize the controllability but also introduce a lack of realism or are complex to automate. On the contrary, GANs that specialize in realism are difficult to control. To overcome these challenges, we propose a novel GAN architecture leveraging any differentiable segmentation model. We demonstrate the genericness of our framework that presents state of the art results on different use cases. It generates images that look realistic while offering a precise color control.
Appendix
Appendix file can be found at here and paper full version here.
Citation
If you find this work useful, please cite our paper:
``` @InProceedings{10.1007/978-3-031-43148-7_41, author="Auriau, Vincent and Malherbe, Emmanuel and Perrot, Matthieu", editor="Foresti, Gian Luca and Fusiello, Andrea and Hancock, Edwin", title="Weak Segmentation-Guided GAN for Realistic Color Edition", booktitle="Image Analysis and Processing -- ICIAP 2023", year="2023", publisher="Springer Nature Switzerland", address="Cham", pages="487--499", }
```
Owner
- Name: Vincent Auriau
- Login: VincentAuriau
- Kind: user
- Repositories: 2
- Profile: https://github.com/VincentAuriau
Citation (CITATION.cff)
repository-code: "https://github.com/VincentAuriau/wsg-gan-color-edition"
message: "If you use this software, please cite it as below."
title: "Weak Segmentation-Guided GAN for Realistic Color Edition"
cff-version: 1.2.0
doi: 10.1007/978-3-031-43148-7_41
authors:
- family-names: "Auriau"
given-names: " Vincent"
- family-names: "Malherbe"
given-names: " Emmanuel"
- family-names: "Perrot"
given-names: " Matthieu"
preferred-citation:
type: "conference-paper"
publisher:
name: "Springer Nature Switzerland"
date-released: "2023-01-01"
title: "Weak Segmentation-Guided GAN for Realistic Color Edition"
booktitle: "Image Analysis and Processing -- ICIAP 2023"
editor: "Foresti, Gian Luca
and Fusiello, Andrea
and Hancock, Edwin"
publisher: "Springer Nature Switzerland"
start: "487"
end: "499"
authors:
- family-names: "Auriau"
given-names: " Vincent"
- family-names: "Malherbe"
given-names: " Emmanuel"
- family-names: "Perrot"
given-names: " Matthieu"
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