A Variational Approach with Nonlocal Self-Similarity and Joint-Sparsity for Hyperspectral Image Super-Resolution

Abstract

The aim of hyperspectral image super-resolution (HSI-SR) is to produce high spatial resolution hyperspectral image (HR - HSI) by exploiting the available high spatial resolution multispectral image (HR-MSI) and low spatial resolution hyperspectral image (LR-HSI). In this work, we develop a novel matrix factorization (MF)-based HSI -SR way, which formulates the HSI -SR problem as estimating the spectral dictionary from the observed LR - HSI and the coefficient matrix from both the observed HR-MSI and LR-HSI. Specifically, we first estimate the spectral dictionary from the observed LR - HSI by the dictionary learning algorithm with redundancy assumption. Moreover, based on the superpixel segmentation technology used in the observed HR-MSI, the coefficient vectors are grouped. By concatenating the joint-sparse, nonlocallow-rank, and nonnegative priors of the grouped coefficient vectors, we develop a novel coefficient matrix estimation variational model, which fully explores the nonlocal self-similarity of the desired HR-HSI. The proposed coefficient matrix estimation model is solved under the alternating direction method of multipliers (ADMM) framework. Experimental results prove the superiority of the proposed way from the quantitative and qualitative analysis.