A Generalized Tensor Formulation for Hyperspectral Image Super-Resolution Under General Spatial Blurring

Hyperspectral super-resolution is commonly accomplished by the fusing of a hyperspectral imaging of low spatial resolution with a multispectral image of high spatial resolution, and many tensor-based approaches to this task have been recently proposed. Yet, it is assumed in such tensor-based methods...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 47; H. 6; S. 4684 - 4698
Hauptverfasser: Wang, Yinjian, Li, Wei, Gui, Yuanyuan, Du, Qian, Fowler, James E.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.06.2025
Schlagworte:
Anisotropic
Degradation
Hyperspectral imaging
hyperspectral super-resolution
Image fusion
Mathematical models
Matrix decomposition
nonconvex surrogate
Optimization
recoverability
sparse coding
Spatial resolution
Superresolution
tensor factorization
Tensors
Vectors
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Zusammenfassung:Hyperspectral super-resolution is commonly accomplished by the fusing of a hyperspectral imaging of low spatial resolution with a multispectral image of high spatial resolution, and many tensor-based approaches to this task have been recently proposed. Yet, it is assumed in such tensor-based methods that the spatial-blurring operation that creates the observed hyperspectral image from the desired super-resolved image is separable into independent horizontal and vertical blurring. Recent work has argued that such separable spatial degradation is ill-equipped to model the operation of real sensors which may exhibit, for example, anisotropic blurring. To accommodate this fact, a generalized tensor formulation based on a Kronecker decomposition is proposed to handle any general spatial-degradation matrix, including those that are not separable as previously assumed. Analysis of the generalized formulation reveals conditions under which exact recovery of the desired super-resolved image is guaranteed, and a practical algorithm for such recovery, driven by a blockwise-group-sparsity regularization, is proposed. Extensive experimental results demonstrate that the proposed generalized tensor approach outperforms not only traditional matrix-based techniques but also state-of-the-art tensor-based methods; the gains with respect to the latter are especially significant in cases of anisotropic spatial blurring.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2025.3545605