Single Frame Super Resolution with Convolutional Neural Network for Remote Sensing Imagery
Jie Ying Fu, Yuhong Liu, Feng Li · 2018
In this paper, a new convolutional neural networks based super resolution(SR) is proposed. SR has been a hot research area for decades, and it includes two types: single frame based SR and multi-frame based SR. The focus of the paper is to reconstruct the corresponding high resolution image from a given low resolution image. The popular end-to-end learning architecture is improved and no preprocessing and image aggregation are needed. Our network model(RSCNN) uses different convolution kernels for a set of feature maps in the feature mapping step, which ensures the accuracy of reconstruction results under the premise of improving the reconstruction quality. The method is applied to Jilin-l which is the first self-developed commercial remote sensing satellite group in China. The results show the superiority of our method both visually and numerically by comparing with other excellent image super resolution algorithms.