Remote Sensing Image Super-Resolution Reconstruction Based on Nonlocal Pairwise Dictionaries and Double Regularization

A nonlocal pairwise dictionary learning (NPDL) model that includes an estimated dictionary and a residual dictionary is applied to remote sensing image super-resolution (SR) reconstruction in this paper. The dictionary pair is trained from some low-resolution (LR) remote sensing images to deal with the lack of high-resolution component in remote sensing images. The reconstructed image has been shown to retain the structural information of the given LR image itself. Moreover, the local and nonlocal (NL) priors are used for image SR to enhance robustness of the pairwise dictionary. Improved NL self-similarity and local kernel constraint regularization terms are introduced to the image optimization process. Using this, the photometric, geometric, and feature information of the given LR image can be taken into consideration to improve the quality of reconstruction. Simulation results show that the proposed algorithm can achieve better visual effects and the average peak signal-to-noise ratio (PSNR) is improved by approximately 0.5 db compared with the state-of-the-art image SR methods.