Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Accelerating Deep Learning in Reconstructive Spectroscopy Using Synthetic Data

Not Accessible

Your library or personal account may give you access

Abstract

A deep learning prototype for a reconstructive spectrometer was developed using an experimentally determined spectrometer response and synthetic data generated from the response matrix. Benchmarking with synthetic and experimental data was performed.

© 2023 The Author(s)

PDF Article
More Like This
Deep learning-based spectral reconstruction on a chip using a scalable plasmonic encoder

Artem Goncharov, Calvin Brown, Zachary Ballard, Mason Fordham, Ashley Clemens, Yunzhe Qiu, Yair Rivenson, and Aydogan Ozcan
FTh1K.4 CLEO: QELS_Fundamental Science (CLEO:FS) 2021

3D Object Reconstruction Using Deep Learning Coherence Holography (DCH)

Quang Trieu and George Nehmetallah
HW3D.5 Digital Holography and Three-Dimensional Imaging (DH) 2023

Synthetic data generation for machine learning of 3D features using neutrons and X-rays

Pinghan Chu, Bradley Thomas Wolfe, David Paul Broughton, Robert Emil Reinovsky, Sky K. Sjue, and Zhehui Wang
DM3A.4 3D Image Acquisition and Display: Technology, Perception and Applications (3D) 2023

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.