A Novel Wasserstein Quaternion Generative Adversarial Network for Color Image Generation
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908701626990592 |
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| author | Jia, Zhigang Wang, Duan Wang, Hengkai Xie, Yajun Zhao, Meixiang Zhao, Xiaoyu |
| author_facet | Jia, Zhigang Wang, Duan Wang, Hengkai Xie, Yajun Zhao, Meixiang Zhao, Xiaoyu |
| contents | Color image generation has a wide range of applications, but the existing generation models ignore the correlation among color channels, which may lead to chromatic aberration problems. In addition, the data distribution problem of color images has not been systematically elaborated and explained, so that there is still the lack of the theory about measuring different color images datasets. In this paper, we define a new quaternion Wasserstein distance and develop its dual theory. To deal with the quaternion linear programming problem, we derive the strong duality form with helps of quaternion convex set separation theorem and quaternion Farkas lemma. With using quaternion Wasserstein distance, we propose a novel Wasserstein quaternion generative adversarial network. Experiments demonstrate that this novel model surpasses both the (quaternion) generative adversarial networks and the Wasserstein generative adversarial network in terms of generation efficiency and image quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08542 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Novel Wasserstein Quaternion Generative Adversarial Network for Color Image Generation Jia, Zhigang Wang, Duan Wang, Hengkai Xie, Yajun Zhao, Meixiang Zhao, Xiaoyu Computer Vision and Pattern Recognition Artificial Intelligence Numerical Analysis Color image generation has a wide range of applications, but the existing generation models ignore the correlation among color channels, which may lead to chromatic aberration problems. In addition, the data distribution problem of color images has not been systematically elaborated and explained, so that there is still the lack of the theory about measuring different color images datasets. In this paper, we define a new quaternion Wasserstein distance and develop its dual theory. To deal with the quaternion linear programming problem, we derive the strong duality form with helps of quaternion convex set separation theorem and quaternion Farkas lemma. With using quaternion Wasserstein distance, we propose a novel Wasserstein quaternion generative adversarial network. Experiments demonstrate that this novel model surpasses both the (quaternion) generative adversarial networks and the Wasserstein generative adversarial network in terms of generation efficiency and image quality. |
| title | A Novel Wasserstein Quaternion Generative Adversarial Network for Color Image Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Numerical Analysis |
| url | https://arxiv.org/abs/2512.08542 |