Open AccessDistortion correction for particle image velocimetry using multiple-input deep convolutional neural network and Hartmann-Shack sensing
Author(s) -
Zeyu Gao,
Hannes Radner,
Lars Büttner,
Hongwei Ye,
Xinyang Li,
Jürgen Czarske
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.419591
Subject(s) - particle image velocimetry , wavefront , adaptive optics , optics , deformable mirror , velocimetry , distortion (music) , particle tracking velocimetry , computer science , physics , actuator , image quality , convolutional neural network , artificial intelligence , computer vision , image (mathematics) , amplifier , turbulence , thermodynamics , computer network , bandwidth (computing)
Aberrations degrade the accuracy of quantitative, imaging-based measurements, like particle image velocimetry (PIV). Adaptive optical elements can in principle correct the wavefront distortions, but are limited by their technical specifications. Here we propose an actuator-free correction based on a multiple-input deep convolutional neural network which uses an additional input from a wavefront sensor to correct time-varying distortions. It is applied for imaging flow velocimetry to conduct measurements through a fluctuating air-water phase boundary. Dataset for neural network is generated by an experimental setup with a deformable mirror. Correction performance of trained model is estimated in terms of image quality, which is improved significantly, and flow measurement results, where the errors induced by the distortion from fluctuating phase boundary can be corrected by 82 %. The technique has the potential to replace classical closed-loop adaptive optical systems where the performance of the actuators is not sufficient.