An undercomplete autoencoder for denoising computational 3D sectional images

Vineela Chandra Dodda; Lakshmi Kuruguntla; Karthikeyan Elumalai; Inbarasan Muniraj; Sunil Chinnadurai

doi:10.1364/3D.2022.JW2A.19

Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP)
Technical Digest Series (Optica Publishing Group, 2022),
paper JW2A.19
•https://doi.org/10.1364/3D.2022.JW2A.19

An undercomplete autoencoder for denoising computational 3D sectional images

Vineela Chandra Dodda, Lakshmi Kuruguntla, Karthikeyan Elumalai, Inbarasan Muniraj, and Sunil Chinnadurai

Propagation Through and Characterization of Atmospheric and Oceanic Phenomena 2022

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

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V. C. Dodda, L. Kuruguntla, K. Elumalai, I. Muniraj, and S. Chinnadurai, "An undercomplete autoencoder for denoising computational 3D sectional images," in Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP), Technical Digest Series (Optica Publishing Group, 2022), paper JW2A.19.

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Abstract

We developed a deep stacked undercomplete autoencoder (i.e., supervised) network to denoise the noisy 3D sectional images. Results demonstrate the feasibility of our proposed model in terms of peak-signal-to-noise ratio.

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