@Article{jqc.2021.017250,
AUTHOR = {Biying Deng
, Desheng Zheng, Zhifeng Liu
, Yanling Lai, Zhihong Zhang},
TITLE = {Incomplete Image Completion through GAN},
JOURNAL = {Journal of Quantum Computing},
VOLUME = {3},
YEAR = {2021},
NUMBER = {3},
PAGES = {119--126},
URL = {http://www.techscience.com/jqc/v3n3/46041},
ISSN = {2579-0145},
ABSTRACT = { There are two difficult in the existing image restoration methods. One 
is that the method is difficult to repair the image with a large damaged, the other 
is the result of image completion is not good and the speed is slow. With the 
development and application of deep learning, the image repair algorithm based 
on generative adversarial networks can repair images by simulating the 
distribution of data. In the process of image completion, the first step is trained the 
generator to simulate data distribution and generate samples. Then a large number 
of falsified images are quickly generated using the generative adversarial network 
and search for the code of the closest damaged image. Finally, the generator 
generates missing content by using this code. On this basis, this paper combines 
the semantic loss function and the perceptual loss function. Experimental result 
show that the method successfully predicts the information of large areas missing 
in the image, and realizes the photorealism, producing clearer and more consistent 
results than previous methods.},
DOI = {10.32604/jqc.2021.017250}
}