Distortion Correction for Imaging Using a Wavefront Sensing Based Multiple-Input Deep Neural Network

Lars Büttner; Zeyu Gao; Ping Yang; Clemens Bilsing; Jürgen Czarske

doi:10.1364/COSI.2023.CTu3B.4

Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP)
Technical Digest Series (Optica Publishing Group, 2023),
paper CTu3B.4
•https://doi.org/10.1364/COSI.2023.CTu3B.4

Distortion Correction for Imaging Using a Wavefront Sensing Based Multiple-Input Deep Neural Network

Lars Büttner, Zeyu Gao, Ping Yang, Clemens Bilsing, and Jürgen Czarske

Computational Optical Sensing and Imaging 2023

Boston, Massachusetts United States
14–17 August 2023
ISBN: 978-1-957171-28-9

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Machine Learned Imaging I (CTu3B)

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L. Büttner, Z. Gao, P. Yang, C. Bilsing, and J. Czarske, "Distortion Correction for Imaging Using a Wavefront Sensing Based Multiple-Input Deep Neural Network," in Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP), Technical Digest Series (Optica Publishing Group, 2023), paper CTu3B.4.

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Abstract

An actuator-free aberration correction using a multiple-input deep convolutional neural network is presented. It uses an additional wavefront sensor input to correct time-varying low-order aberrations. These advantages were transferred to aberration-corrected imaging velocimetry.

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