Fast Hadamard Transforms for Compressive Sensing of Joint Systems: Measurement of a 3.2 Million-Dimensional Bi-Photon Probability Distribution

Authors

Daniel J. Lum, University of Rochester
Samuel H. Knarr, University of Rochester
John C. Howell, Chapman UniversityFollow

Document Type

Article

Publication Date

10-13-2015

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.

Comments

This article was originally published in Optics Express, volume 23, issue 21, in 2015. https://doi.org/10.1364/OE.23.027636

Recommended Citation

D. J. Lum, S. H. Knarr, and J. C. Howell, Fast Hadamard Transforms for Compressive Sensing of Joint Systems: Measurement of a 3.2 Million-Dimensional Bi-Photon Probability Distribution, Optics Express 23(21), 27636-27649. https://doi.org/10.1364/OE.23.027636

Peer Reviewed

1

Copyright

Optica