Hyperspectral super-resolution via coupled tensor ring factorization

Abstract Hyperspectral super-resolution (HSR) fuses a low-resolution hyperspectral image (HSI) and a high-resolution multispectral image (MSI) to obtain a high-resolution HSI (HR-HSI). In this paper, we propose a new model called coupled tensor ring factorization (CTRF) for HSR. The proposed CTRF approach simultaneously learns the tensor ring core tensors of the HR-HSI from a pair of HSI and MSI. The CTRF model can separately exploit the low-rank property of each class (Section 3.3), which has not been explored in previous coupled tensor models. Meanwhile, the model inherits the simple representation of coupled matrix/canonical polyadic factorization and flexible low-rank exploration of coupled Tucker factorization. We further introduce spectral nuclear norm regularization to explore the global spectral low-rank property. The experiments demonstrated the advantage of the proposed nuclear norm regularized CTRF model compared to previous matrix/tensor and deep learning methods.