Sparse representation-based dictionary learning methods for hyperspectral super-resolution

摘要

Due to hardware limitations, hyperspectral imagery has low spatial resolution. It can be obtained super- resolution hyperspectral imagery by means of sparse representation-based methods that are designed for improving spatial resolution. In this paper, the effect of sparse representation-based dictionary learning algorithms including K-SVD, ODL and Bayes on obtaining super-resolution images with low error and high quality has been investigated. The method with best results has been identified.