Remote Sensing Images Super-resolution Based on Sparse Dictionaries and Residual Dictionaries

In this paper, a sensing image super-resolution (SR) reconstruction method is proposed. Sparse dictionary dealing with remote sensing image SR problem is introduced in this work. The sparse dictionary is based on a sparsity model where the dictionary atoms have sparse representation over a basic dictionary. The sparse dictionary consists of two parts: basic dictionary and atom representation matrix. The sparse dictionary leads to compact representation and it is both adaptive and efficient. Furthermore, compared with conventional SR methods, two dictionary pairs, i.e. primitive sparse dictionary pair and residual sparse dictionary pair, are proposed. The primitive sparse dictionary pair is learned to reconstruct initial high-resolution (HR) remote sensing image from a single low-resolution (LR) input. However, the initial HR remote sensing image loses some details compare with the corresponding original HR image completely. Therefore, residual sparse dictionary pair is learned to reconstruct residual information. The proposed method is tested on remote sensing images, and the experimental results indicate that the proposed algorithm can provide substantial improvement in resolution of remote sensing images, and the results are superior in quality to the results produced by other methods.