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Multi-Generator Discriminator Network Using Texture-Edge Information

Kyeongseok Jang1, Seongsoo Cho2, Kwang Chul Son3,*

1 Department of Plasma Bio Display, Kwangwoon University, 20, Gwangun-ro, Nowon-gu, Seoul, 01897, Korea
2 School of Software, Soongsil University, 369, Sangdo-ro, Dongjak-gu, Seoul, 06978, Korea
3 Department of Information Contents, Kwangwoon University, 20, Gwangun-ro, Nowon-gu, Seoul, 01897, Korea

* Corresponding Author: Kwang Chul Son. Email:

Computers, Materials & Continua 2023, 75(2), 3537-3551.


In the proposed paper, a parallel structure type Generative Adversarial Network (GAN) using edge and texture information is proposed. In the existing GAN-based model, many learning iterations had to be given to obtaining an output that was somewhat close to the original data, and noise and distortion occurred in the output image even when learning was performed. To solve this problem, the proposed model consists of two generators and three discriminators to propose a network in the form of a parallel structure. In the network, each edge information and texture information were received as inputs, learning was performed, and each character was combined and outputted through the Combine Discriminator. Through this, edge information and distortion of the output image were improved even with fewer iterations than DCGAN, which is the existing GAN-based model. As a result of learning on the network of the proposed model, a clear image with improved contour and distortion of objects in the image was output from about 50,000 iterations.


Cite This Article

K. Jang, S. Cho, K. C. Son, K. Jang, S. Cho et al., "Multi-generator discriminator network using texture-edge information," Computers, Materials & Continua, vol. 75, no.2, pp. 3537–3551, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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