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Proceedings Paper

Design and evaluation of task-specific compressive optical systems
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Paper Abstract

Many optical systems are used for specific tasks such as classification. Of these systems, the majority are designed to maximize image quality for human observers; however, machine learning classification algorithms do not require the same data representation used by humans. In this work we investigate compressive optical systems optimized for a specific machine sensing task. Two compressive optical architectures are examined: an array of prisms and neutral density filters where each prism and neutral density filter pair realizes one datum from an optimized compressive sensing matrix, and another architecture using conventional optics to image the aperture onto the detector, a prism array to divide the aperture, and a pixelated attenuation mask in the intermediate image plane. We discuss the design, simulation, and tradeoffs of these compressive imaging systems built for compressed classification of the MNSIT data set. To evaluate the tradeoffs of the two architectures, we present radiometric and raytrace models for each system. Additionally, we investigate the impact of system aberrations on classification accuracy of the system. We compare the performance of these systems over a range of compression. Classification performance, radiometric throughput, and optical design manufacturability are discussed.

Paper Details

Date Published: 13 May 2019
PDF: 12 pages
Proc. SPIE 10990, Computational Imaging IV, 109900H (13 May 2019); doi: 10.1117/12.2520187
Show Author Affiliations
Brian J. Redman, Sandia National Labs. (United States)
Gabriel C. Birch, Sandia National Labs. (United States)
Charles F. LaCasse, Sandia National Labs. (United States)
Amber L. Dagel, Sandia National Labs. (United States)
Tu-Thach Quach, Sandia National Labs. (United States)
Meghan Galiardi, Sandia National Labs. (United States)

Published in SPIE Proceedings Vol. 10990:
Computational Imaging IV
Abhijit Mahalanobis; Lei Tian; Jonathan C. Petruccelli, Editor(s)

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