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Award Abstract # 2238485
CAREER: Mixed-State Computational Imaging and Light Transport Probing

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: CARNEGIE MELLON UNIVERSITY
Initial Amendment Date: February 16, 2023
Latest Amendment Date: May 28, 2024
Award Number: 2238485
Award Instrument: Continuing Grant
Program Manager: Jie Yang
jyang@nsf.gov
 (703)292-4768
IIS
 Division of Information & Intelligent Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: March 1, 2023
End Date: February 29, 2028 (Estimated)
Total Intended Award Amount: $599,952.00
Total Awarded Amount to Date: $249,550.00
Funds Obligated to Date: FY 2023 = $139,398.00
FY 2024 = $110,152.00
History of Investigator:
  • Matthew O'Toole (Principal Investigator)
    motoole2@andrew.cmu.edu
Recipient Sponsored Research Office: Carnegie-Mellon University
5000 FORBES AVE
PITTSBURGH
PA  US  15213-3815
(412)268-8746
Sponsor Congressional District: 12
Primary Place of Performance: Carnegie Mellon University
5000 FORBES AVE
PITTSBURGH
PA  US  15213-3890
Primary Place of Performance
Congressional District:
12
Unique Entity Identifier (UEI): U3NKNFLNQ613
Parent UEI: U3NKNFLNQ613
NSF Program(s): Robust Intelligence
Primary Program Source: 01002324DB NSF RESEARCH & RELATED ACTIVIT
01002425DB NSF RESEARCH & RELATED ACTIVIT

01002526DB NSF RESEARCH & RELATED ACTIVIT

01002627DB NSF RESEARCH & RELATED ACTIVIT

01002728DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 1045, 7495
Program Element Code(s): 749500
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Computational imaging is a field that combines optics, electronics, and computational processing to capture new forms of visual information. The theoretical underpinning of any computational imaging technique is its model for light propagation. Vision and graphics research generally adopts a geometric optics approximation of light transport, where light travels along straight lines until it undergoes a scattering event or gets absorbed. At small scales, the wave-like properties of light become far more noticeable and require modeling light as a wave at the cost of additional computational overhead. In practice, imaging systems are neither perfectly incoherent (as in geometric optics) nor completely coherent (as in wave optics). Light is more aptly modeled as a state mixture: a statistical ensemble of coherent waves. This project develops a comprehensive theory and corresponding imaging techniques to leverage state mixtures for a wide variety of imaging applications, including the development of new computational microscopes, 3D sensors, and optical vibration sensors. Moreover, this project connects techniques used in both the incoherent and coherent settings, drawing upon imaging research developed across multiple different research communities. In addition, the project will develop a low-cost, open-source projector-camera platform to easily implement a collection of important computational imaging techniques. This platform will be used to educate students at all levels (graduate, undergraduate, and K-12).

This project tackles three independent, but tightly coupled, research thrusts focused on (1) mixed-state sensing, (2) mixed-state illumination, and (3) mixed-state scene analysis. The first thrust explores state mixtures observed at the sensor. Many imaging techniques depend on the incident light being coherent (such as in coherent diffraction imaging), and do not account for the incident light having multiple wavelengths or polarization states. This thrust develops flexible imaging systems and reconstruction algorithms that can uniquely recover complex fields from incoherent mixtures of multiple coherent fields. The second thrust investigates a new form of transport probing based on programming mixed-state illumination. Transport probing provides a camera the ability to enhance or attenuate specific light paths. This thrust develops a fundamentally different approach to probing that involves selectively interfering pairs of light paths, which has applications in 3D sensing and the separation of direct and global light paths. The third thrust uses mixed-state scene analysis techniques to infer scene dynamics, which includes optically amplifying and measuring the imperceptible vibrations of objects. A focus of this thrust is to capture vibration measurements at higher spatial resolutions, in a more light-efficient fashion, and across up to six dimensions of motion, providing more and better information about visual scenes.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Chan, Dorian and O'Toole, Matthew "Light-Efficient Holographic Illumination for Continuous-Wave Time-of-Flight Imaging" , 2023 https://doi.org/10.1145/3610548.3618152 Citation Details
Chan, Dorian and Sheinin, Mark and OToole, Matthew "SpinCam: High-Speed Imaging via a Rotating Point-Spread Function" , 2023 https://doi.org/10.1109/ICCV51070.2023.00990 Citation Details
Shandilya, Aarrushi and Attal, Benjamin and Richardt, Christian and Tompkin, James and OToole, Matthew "Neural Fields for Structured Lighting" , 2023 https://doi.org/10.1109/ICCV51070.2023.00325 Citation Details

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