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.

Neural networks have strong nonlinear learning ability, so the super-resolution algorithms based on neural networks are preliminarily studied. These algorithms can only be used in controlled microscanning, which has uniform displacement between frames. It is difficult to apply these algorithms to uncontrolled microscanning. In order to overcome the limiting condition and obtain better super-resolution performance, a deblurring algorithm using Radial Basis Function (RBF) neural network was firstly proposed, which was then combined with non-uniform interpolation step to form a new two-step super-resolution algorithm. The simulation results show that the Structural SIMilarity (SSIM) index of proposed algorithm is 0.55-0.7. The proposed two-step super-resolution algorithm not only extends application scope of RBF neural network but also achieves good super-resolution performance.