首页|Hyperspectral super-resolution via coupled tensor ring factorization
Hyperspectral super-resolution via coupled tensor ring factorization
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NSTL
Elsevier
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 simul-taneously 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 prop-erty. The experiments demonstrated the advantage of the proposed nuclear norm regularized CTRF model compared to previous matrix/tensor and deep learning methods. (c) 2021 Elsevier Ltd. All rights reserved.
Coupled tensor ring decompositionSuper-resolutionHyperspectralMultispectralNONNEGATIVE MATRIX FACTORIZATIONIMAGESFUSION
NSTL
Elsevier
