Optics-Free Imaging Using A Self-Consistent Supervised Deep Neural Network

Soren Nelson; Rajesh Menon

doi:10.1364/AIS.2021.JTu5A.3

OSA Optical Sensors and Sensing Congress 2021 (AIS, FTS, HISE, SENSORS, ES)
OSA Technical Digest (Optica Publishing Group, 2021),
paper JTu5A.3
•https://doi.org/10.1364/AIS.2021.JTu5A.3

Optics-Free Imaging Using A Self-Consistent Supervised Deep Neural Network

Soren Nelson and Rajesh Menon

Optics and Photonics for Sensing the Environment 2021

Washington, DC United States
19–23 July 2021
ISBN: 978-1-943580-90-3

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Joint Poster Session I (JTu5A)

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S. Nelson and R. Menon, "Optics-Free Imaging Using A Self-Consistent Supervised Deep Neural Network," in OSA Optical Sensors and Sensing Congress 2021 (AIS, FTS, HISE, SENSORS, ES), S. Buckley, F. Vanier, S. Shi, K. Walker, I. Coddington, S. Paine, K. Lok Chan, W. Moses, S. Qian, P. Pellegrino, F. Vollmer, G. , J. Jágerská, R. Menzies, L. Emmenegger, and J. Westberg, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper JTu5A.3.

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Abstract

We propose a deep neural network self-consistent supervised model for optics-free image reconstruction. The model learns both the inverse imaging problem as well as the forward to better constrain the reconstruction.

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