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• W4307741723 abstract "We present a new method for solving simultaneously hyperspectral super-resolution and spectral unmixing of the unknown super-resolution image. Our method relies on three key elements: (1) the nonnegative decomposition in rank-(L <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>r</inf> , L <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>r</inf> , 1) block-terms, (2) joint tensor factorization with multiplicative updates, and (3) the formulation of a family of optimization problems with β-divergences objective functions. We come up with a family of simple, robust and efficient algorithms, adaptable to various noise statistics. Experiments show that our approach competes favorably with state-of-the-art methods for solving both problems at hand for various noise statistics." @default.
• W4307741723 created "2022-11-05" @default.
• W4307741723 creator A5005234532 @default.
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• W4307741723 date "2023-06-04" @default.
• W4307741723 modified "2023-10-12" @default.
• W4307741723 title "Nonnegative Block-Term Decomposition with the β-Divergence: Joint Data Fusion and Blind Spectral Unmixing" @default.
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• W4307741723 doi "https://doi.org/10.1109/icassp49357.2023.10096100" @default.
• W4307741723 hasPublicationYear "2023" @default.
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