Remote Sensing Image Super-resolution Based on Sparse Representation

¹ College of Electronic Engineering, Heilongjiang University, Harbin, 150080, China
² Heilongjiang Duobaoshan Copper Industry lnc., Heihe, 164300, China

^a Corresponding author: Email: zhufuzhen@hlju.edu.cn. Zhu Fuzhen (1978-), doctor, master supervisor, research interests : image super-resolution, compressive sensing, neural network, deep learning, et al.

Abstract

In order to obtain higher resolution remote sensing images with more details, an improved sparse representation remote sensing image super-resolution reconstruction(SRR) algorithm is proposed. First, remote sensing image is preprocessed to obtain the required training sample image; then, the KSVD algorithm is used for dictionary training to obtain the high-low resolution dictionary pairs; finally, the image feature extraction block is represented, which is improved by using adaptive filtering method. At the same time, the mean value filtering method is used to improve the super-resolution reconstruction iterative calculation. Experiment results show that, compared with the most advanced sparse representation super-resolution algorithm, the improved sparse representation super-resolution method can effectively avoid the loss of edge information of SRR image and obtain a better super-resolution reconstruction effect. The texture details are more abundant in subjective vision, the PSNR is increased about 1 dB, and the structure similarity (SSIM) is increased about 0.01.