A Generalized Tensor Formulation for Hyperspectral Image Super-Resolution Under General Spatial Blurring
<|reference_start|>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 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.<|reference_end|>
@article{wang2024a,
    title={A Generalized Tensor Formulation for Hyperspectral Image
  Super-Resolution Under General Spatial Blurring},
    author={Yinjian Wang, Wei Li, Yuanyuan Gui, Qian Du and James E. Fowler},
    journal={arXiv preprint arXiv:2409.18731},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18731},
    primaryClass={eess.IV cs.CV}
}