Non-local sparse representation for hyperspectral image super-resolution

In this paper, a non-local based sparse representation (called as the NLSR) is proposed for the super-resolution of hyperspectral image. Specifically, the NLSR firstly uses the non-local Kmeans to partition pixels of low spatial resolution hyperspectral image into several classes. The non-local Kmeans can exploit the similar patterns and structures of the low spatial resolution image to enhance the efficiency of the sparse solution. Then, the sparse representation is independently applied on each class of the low resolution hyperspectral image and high spatial resolution multispectral image to obtain the high resolution hyperspectral image. Experimental results demonstrate the superiority of the proposed NLSR method over several well-known super-resolution methods.