摘要
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.
基金
supported in part by the National Natural Science Foundation of China under Grant 61806099
in part by the Natural Science Foundation of Jiangsu Province of China under Grant BK20180790,in part by the Natural Science Research of Jiangsu Higher Education Institutions of China under Grant 8KJB520033
in part by Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology under Grant 2243141701077.