Towards a Universal Data Training Set for Coded-Diffraction Image Reconstruction and No-Hidden-Layer Neural Networks

Baurzhan Muminov; Altai Perry; Rakib Hyder; M. Salman Asif; Luat T. Vuong

doi:10.1364/ISA.2021.ITh5D.5

OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)
OSA Technical Digest (Optica Publishing Group, 2021),
paper ITh5D.5
•https://doi.org/10.1364/ISA.2021.ITh5D.5

Towards a Universal Data Training Set for Coded-Diffraction Image Reconstruction and No-Hidden-Layer Neural Networks

Baurzhan Muminov, Altai Perry, Rakib Hyder, M. Salman Asif, and Luat T. Vuong

Imaging Systems and Applications 2021

Washington, DC United States
19–23 July 2021
ISBN: 978-1-943580-89-7

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Machine Learning and Imaging II (ITh5D)

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B. Muminov, A. Perry, R. Hyder, M. S. Asif, and L. T. Vuong, "Towards a Universal Data Training Set for Coded-Diffraction Image Reconstruction and No-Hidden-Layer Neural Networks," in OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP), H. Hua, B. Javidi, M. Martinez-Corral, O. Matoba, A. Stern, S. Thibault, T. Alieva, J. Ke, F. Willomitzer, F. Okten, P. Silveira, P. Banerjee, E. Stoykova, D. Chu, J. Park, F. Imai, C. Joo, M. Digman, D. Gardner, S. Gladysz, D. LeMaster, S. Basu, and O. Korotkova, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper ITh5D.5.

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Abstract

The stability of an inverse-problem solver depends on algorithm computational complexity. We demonstrate generalizable image reconstruction with the simplest of hybrid machine vision systems: fixed, linear optical preprocessors combined with no-hidden-layer,”small-brain” neural networks.

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