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Super-resolution algorithm for remote sensing images based on compressive sensing in wavelet domain
YANG Xuefeng, CHENG Yaoyu, WANG Gao
Journal of Computer Applications
2017, 37 (5):
1430-1433.
DOI: 10.11772/j.issn.1001-9081.2017.05.1430
Focused on the issue that complex image texture can not be fully expressed by single dictionary in image Super-Resolution (SR) reconstruction, a remote sensing image super-resolution algorithm based on compressive sensing and wavelet theory using multiple dictionaries was proposed. Firstly, the
K-Singular Value Decomposition (
K-SVD) algorithm was used to establish the different dictionaries in the different frequency bands in wavelet domain. Secondly, the initial solution of SR image was obtained by using global limited condition. Finally, the sparse solution of multiple dictionaries in wavelet domain was implemented using Orthogonal Matching Pursuit (OMP) algorithm. The experimental results show that the proposed algorithm presents the better subjective visual effect compared with the single dictionary based algorithm. The Peak Signal-to-Noise Ratio (PSNR) and the Structural SIMilarity (SSIM) index increase more than 2.8 dB and 0.01 separately. The computation time is reduced as the dictionaries can be used once again.
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