Improved Sparse Representation Super-Resolution algorithm for Remote Sensing Image

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 quality super-resolution reconstruction (SRR) of remote sensing images, an improved sparse representation remote sensing images SRR method is proposed in this paper. First, low-resolution image is processed by improved feature extract operator. The high-resolution image and low-resolution image blocks have the same sparse representation coefficient, so the SRR image with higher spatial resolution can be derived from the sparse representation coefficients which have been obtained from low-resolution image. The improved feature extraction operator is a method to get more detail and texture information from the training images. Experiment results show that more texture details can be obtained in the result of SRR remote sensing images subjectively. At the same time, the objective evaluation parameters are improved greatly. The peak PSNR is increased about 2.50dB and 0.50 dB, RMSE is decreased about 2.80 and 0.3 compared with bicubic interpolation algorithm and Ref[8] algorithm respectively.