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

---