Multi-Generator Discriminator Network Using Texture-Edge Information  

作　　者：Kyeongseok Jang Seongsoo Cho Kwang Chul Son 

机构地区：[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

出　　处：《Computers, Materials & Continua》2023年第5期3537-3551,共15页计算机、材料和连续体（英文）

基　　金：supported by the Mid-Career Researcher program through the National Research Foundation of Korea(NRF)funded by the MSIT(Ministry of Science and ICT)under Grant 2020R1A2C2014336.

摘　　要：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.

关 键 词：Deep learning convolution neural network generative adversarial network edge information texture information 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]