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

• URL: http://arxiv.org/abs/2409.18731v1
• Date: Fri, 27 Sep 2024 13:23:17 GMT
• Title: A Generalized Tensor Formulation for Hyperspectral Image Super-Resolution Under General Spatial Blurring
• Authors: Yinjian Wang, Wei Li, Yuanyuan Gui, Qian Du, James E. Fowler,
• Abstract summary: Hyperspectral super-resolution is commonly accomplished by fusing a hyperspectral imaging of low spatial resolution with a multispectral image of high spatial resolution. 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.
• Score: 9.163087502142107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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