SVD Entropy Indicates Coded Diffraction Generalized Reconstruction Accuracy

Altai Perry; Xiaojing Weng; Baurzhan Muminov; Luat T. Vuong

doi:10.1364/COSI.2022.CF2C.2

Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP)
Technical Digest Series (Optica Publishing Group, 2022),
paper CF2C.2
•https://doi.org/10.1364/COSI.2022.CF2C.2

SVD Entropy Indicates Coded Diffraction Generalized Reconstruction Accuracy

Altai Perry, Xiaojing Weng, Baurzhan Muminov, and Luat T. Vuong

Computational Optical Sensing and Imaging 2022

Vancouver, British Columbia Canada
11–15 July 2022
ISBN: 978-1-957171-09-8

From the session
Spectral Imaging and Machine Learning for Sensing and Imaging (Joint Theme COSI and LACSEA on Machine Learning) (CF2C)

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A. Perry, X. Weng, B. Muminov, and L. T. Vuong, "SVD Entropy Indicates Coded Diffraction Generalized Reconstruction Accuracy," in Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP), Technical Digest Series (Optica Publishing Group, 2022), paper CF2C.2.

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Abstract

We study encoded diffraction with shallow neural networks using singular value decomposition entropy (H_SVD) to measure image span. Higher-H_SVD synthetic training images are learned more slowly; their generalized models generally attain higher reconstruction accuracy.

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