Work

Fast Hadamard transforms for compressive sensing of joint systems: measurement of a 32 million-dimensional bi-photon probability distribution

Year: 2015

Type: article

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.

Source: Optics Express

DOI: 10.1364/oe.23.027636

Authors: Daniel J. Lum, Samuel H. Knarr, John C. Howell

Institution: University of Rochester

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funder: Air Force Office of Scientific Research