Table of Content

Open Access iconOpen Access



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: email

Journal of New Media 2021, 3(1), 29-39.


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.


Cite This Article

APA Style
Zhang, J., Wang, Z., Zheng, Y., Zhang, G. (2021). Design of network cascade structure for image super-resolution. Journal of New Media, 3(1), 29-39.
Vancouver Style
Zhang J, Wang Z, Zheng Y, Zhang G. Design of network cascade structure for image super-resolution. J New Media . 2021;3(1):29-39
IEEE Style
J. Zhang, Z. Wang, Y. Zheng, and G. Zhang "Design of Network Cascade Structure for Image Super-Resolution," J. New Media , vol. 3, no. 1, pp. 29-39. 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1513


  • 1215


  • 0


Share Link