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    This study introduces a novel tensor factorization method for hyperspectral image super-resolution (HSR). The approach effectively fuses low-resolution and high-resolution data, improving HSR performance and adaptively determining model rank.

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    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Signal Processing

    Background:

    • Hyperspectral image super-resolution (HSR) commonly fuses low-resolution (LR) HSI with high-resolution (HR) MSI.
    • Existing tensor-based HSR methods face challenges in rank determination and modeling capacity.

    Purpose of the Study:

    • To develop an advanced HSR method addressing limitations of current tensor-based approaches.
    • To simultaneously exploit global spectral correlations and nonlocal spatial similarities in HSI data.

    Main Methods:

    • Construction of nonlocal patch tensors (NPTs) to capture spatial-spectral information.
    • Coupled Bayesian tensor factorization to characterize low-rank structures.
    • A hierarchical probabilistic framework using Canonical Polyadic (CP) factorization with automatic relevance determination.
    • Expectation-maximization algorithm for parameter estimation.

    Main Results:

    • The proposed model successfully infers the latent CP rank of NPTs adaptively.
    • Demonstrated superiority in fusion performance on both synthesized and real HSI datasets.
    • Effective characterization of low-rank structures within NPTs.

    Conclusions:

    • The novel tensor factorization framework offers robust HSR by addressing rank determination and modeling capacity issues.
    • The method effectively integrates global spectral and nonlocal spatial information for enhanced HSI fusion.
    • The adaptive rank inference capability eliminates the need for manual parameter tuning, simplifying application.