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• W3040759966 abstract "Compressive sensing (CS) enables us to reconstruct a signal from a few number of measurements obtained from a random or deterministic measurement matrix. Knowledge of the sparsifying basis of the signal is required for the recovery process. In this work, we use a recently developed deterministic measurement matrix and demonstrate recovery of the original signal from compressed samples without the knowledge of the sparsifying basis or the order of sparsity. We experimented this recovery on the Biomedical images. Using smoothed ℓ <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</inf> -norm (SL0) as a recovery algorithm, the original images were recovered from CS measurements with high quality." @default.
• W3040759966 created "2020-07-16" @default.
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• W3040759966 date "2020-06-01" @default.
• W3040759966 modified "2023-10-02" @default.
• W3040759966 title "Blind Deterministic Compressive Sensing for Biomedical Images" @default.
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• W3040759966 doi "https://doi.org/10.1109/memea49120.2020.9137191" @default.
• W3040759966 hasPublicationYear "2020" @default.
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