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• W1919432017 abstract "We demonstrate how to efficiently implement extremely high-dimensional compressive imaging of a bi-photon probability distribution. Our method uses fast-Hadamard-transform Kronecker-based compressive sensing to acquire the joint space distribution. We list, in detail, the operations necessary to enable fast-transform-based matrix-vector operations in the joint space to reconstruct a 16.8 million-dimensional image in less than 10 minutes. Within a subspace of that image exists a 3.2 million-dimensional bi-photon probability distribution. In addition, we demonstrate how the marginal distributions can aid in the accuracy of joint space distribution reconstructions." @default.
• W1919432017 created "2016-06-24" @default.
• W1919432017 creator A5001542611 @default.
• W1919432017 creator A5042591542 @default.
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• W1919432017 date "2015-10-13" @default.
• W1919432017 modified "2023-10-02" @default.
• W1919432017 title "Fast Hadamard transforms for compressive sensing of joint systems: measurement of a 32 million-dimensional bi-photon probability distribution" @default.
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• W1919432017 doi "https://doi.org/10.1364/oe.23.027636" @default.
• W1919432017 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26480426" @default.
• W1919432017 hasPublicationYear "2015" @default.
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