@Article{jnm.2021.018383,
AUTHOR = {Jianwei Zhang, Zhenxing Wang, Yuhui Zheng, Guoqing Zhang},
TITLE = {Design of Network Cascade Structure for Image Super-Resolution},
JOURNAL = {Journal of New Media},
VOLUME = {3},
YEAR = {2021},
NUMBER = {1},
PAGES = {29--39},
URL = {http://www.techscience.com/JNM/v3n1/41732},
ISSN = {2579-0129},
ABSTRACT = {Image super resolution is an important field of computer research. The 
current mainstream image super-resolution technology is to use deep learning to 
mine the deeper features of the image, and then use it for image restoration.
However, most of these models mentioned above only trained the images in a 
specific scale and do not consider the relationships between different scales of
images. In order to utilize the information of images at different scales, we 
design a cascade network structure and cascaded super-resolution convolutional 
neural networks. This network contains three cascaded FSRCNNs. Due to each 
sub FSRCNN can process a specific scale image, our network can 
simultaneously exploit three scale images, and can also use the information of 
three different scales of images. Experiments on multiple datasets confirmed that 
the proposed network can achieve better performance for image SR.},
DOI = {10.32604/jnm.2021.018383}
}