Open Access
ARTICLE
Design of Network Cascade Structure for Image Super-Resolution
Jianwei Zhang, Zhenxing Wang, Yuhui Zheng, Guoqing Zhang*
Nanjing University of Information Science and Technology, Nanjing, 210044, China
* Corresponding Author: Guoqing Zhang. Email:
Journal of New Media 2021, 3(1), 29-39. https://doi.org/10.32604/jnm.2021.018383
Received 06 March 2021; Accepted 07 March 2021; Issue published 15 March 2021
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.
Keywords
Cite This Article
J. Zhang, Z. Wang, Y. Zheng and G. Zhang, "Design of network cascade structure for image super-resolution,"
Journal of New Media, vol. 3, no.1, pp. 29–39, 2021. https://doi.org/10.32604/jnm.2021.018383