Hyperspectral and Multispectral Image Fusion Using Non-Convex Relaxation Low Rank and Total Variation Regularization

Hyperspectral (HS) and multispectral (MS) image fusion is an important task to construct an HS image with high spatial and spectral resolutions. In this paper, we present a novel HS and MS fusion method using non-convex low rank tensor approximation and total variation regularization. In specific, the Laplace based low-rank model is formed to exploit spatial-spectral correlation and nonlocal similarity of the HS image, and the second-order total variation is used to describe the local smoothness structure in the spatial domain and adjacent bands. Also, an effective optimization algorithm is designed for the proposed model. In the experiments, we demonstrate the superiority of the proposed method compared to several state-of-the-art approaches.