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.
Highlights
IntroductionImage super-resolution reconstruction (SRR) is a very important image processing technology
With the development of science, people need digital images with more informatio
The images required for research are all high-resolution, so improving the resolution of remote sensing images to obtain more information has become a hot topic of current research
Summary
Image super-resolution reconstruction (SRR) is a very important image processing technology. The images required for research are all high-resolution, so improving the resolution of remote sensing images to obtain more information has become a hot topic of current research. There are mainly three methods for image super-resolution reconstruction. They are respectively based on interpolation, reconstruction, and learning[3]. The algorithm proposed by Yang[8] in reference[8] has made a great contribution to the research progress of image super-resolution,which refers to directly find the sparse coefficients that can collectively represent low- and high-resolution images in term of the learned dictionary, so as to complete the super-resolution reconstruction of the images. The reconstruction process is improved on the basis of Yang’s algorithm in reference[8] to obtain higher spatial resolution image
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