Hyperspectral image fusion via weighted nuclear norm regularized sparse matrix factorization

The fusion of a low spatial resolution hyperspectral image (LR-HSI) and a high spatial resolution multispectral image (HR-MSI) in the same scene is a common method to get a high spatial resolution hyperspectral image (HR-HSI). For the drawback that the standard nuclear norm regularization treats each singular value equally, this paper proposes a weighted nuclear norm model based on sparse matrix factorization (called WNNS) for hyperspectral image fusion. Specifically, we promote the sparsity of fused images by adding the ℓ1 norm of coefficients. Furthermore, to preserve important data components, we combine with the weighted nuclear norm regularization, where different weights are given to singular values. To efficiently solve the proposed model, we apply an alternating direction method of multipliers (ADMM). Experiments show that the proposed method has better performances in terms of numerical results and visual effects.