@Article{cmc.2020.09754,
AUTHOR = {Ming Zhao, Xinhong Liu, Xin Yao, Kun He},
TITLE = {Better Visual Image Super-Resolution with Laplacian Pyramid of  Generative Adversarial Networks},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {1601--1614},
URL = {http://www.techscience.com/cmc/v64n3/39447},
ISSN = {1546-2226},
ABSTRACT = {Although there has been a great breakthrough in the accuracy and speed of 
super-resolution (SR) reconstruction of a single image by using a convolutional neural 
network, an important problem remains unresolved: how to restore finer texture details 
during image super-resolution reconstruction? This paper proposes an Enhanced 
Laplacian Pyramid Generative Adversarial Network (ELSRGAN), based on the 
Laplacian pyramid to capture the high-frequency details of the image. By combining 
Laplacian pyramids and generative adversarial networks, progressive reconstruction of 
super-resolution images can be made, making model applications more flexible. In order 
to solve the problem of gradient disappearance, we introduce the Residual-in-Residual 
Dense Block (RRDB) as the basic network unit. Network capacity benefits more from 
dense connections, is able to capture more visual features with better reconstruction 
effects, and removes BN layers to increase calculation speed and reduce calculation 
complexity. In addition, a loss of content driven by perceived similarity is used instead of 
content loss driven by spatial similarity, thereby enhancing the visual effect of the superresolution image, making it more consistent with human visual perception. Extensive 
qualitative and quantitative evaluation of the baseline datasets shows that the proposed 
algorithm has higher mean-sort-score (MSS) than any state-of-the-art method and has 
better visual perception.},
DOI = {10.32604/cmc.2020.09754}
}