Quaternion Generative Adversarial Neural Networks and Applications to Color Image Inpainting

Autor:	Wang, Duan, Zhu, Dandan, Zhao, Meixiang, Jia, Zhigang
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Color image inpainting is a challenging task in imaging science. The existing method is based on real operation, and the red, green and blue channels of the color image are processed separately, ignoring the correlation between each channel. In order to make full use of the correlation between each channel, this paper proposes a Quaternion Generative Adversarial Neural Network (QGAN) model and related theory, and applies it to solve the problem of color image inpainting with large area missing. Firstly, the definition of quaternion deconvolution is given and the quaternion batch normalization is proposed. Secondly, the above two innovative modules are applied to generate adversarial networks to improve stability. Finally, QGAN is applied to color image inpainting and compared with other state-of-the-art algorithms. The experimental results show that QGAN has superiority in color image inpainting with large area missing. Comment: 19 pages, 6 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.11567 Zobrazit plný text záznamu View this record from Arxiv