Hyperspectral Super-Resolution With Coupled Tucker Approximation: Recoverability and SVD-Based Algorithms
Clémence Prévost, Konstantin Usevich, Pierre Comon, David Brie
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.
Topics & Concepts
Multilinear mapTucker decompositionHyperspectral imagingSingular value decompositionTensor (intrinsic definition)Multilinear algebraAlgorithmRank (graph theory)SuperresolutionComputer scienceApproximation algorithmImage resolutionMathematicsPattern recognition (psychology)Artificial intelligenceImage (mathematics)Tensor decompositionAlgebra over a fieldCombinatoricsGeometryFiltered algebraDivision algebraPure mathematicsSparse and Compressive Sensing TechniquesImage and Signal Denoising MethodsAdvanced Image Processing Techniques