Hyperspectral Super-Resolution with Coupled Tucker Approximation: Recoverability and SVD-based algorithms

Abstract

We propose a novel approach for hyperspectral super-resolution, that is based on low-rank tensor approximation for a coupled low-rank multilinear (Tucker) model. We show that the correct recovery holds for a wide range of multilinear ranks. For coupled tensor approximation, we propose two SVD-based algorithms that are simple and fast, but with a performance comparable to the state-of-the-art methods. The approach is applicable to the case of unknown spatial degradation and to the pansharpening problem.

arXiv commentIEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, in Press

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• Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms
• Deep Hyperspectral and Multispectral Image Fusion with Inter-image Variability
• Recent Advances and New Guidelines on Hyperspectral and Multispectral Image Fusion
• MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition
• The Tensor Track VII: From Quantum Gravity to Artificial Intelligence
• Single Image Super-Resolution of Noisy 3D Dental CT Images Using Tucker Decomposition