Deep learning based wavefront sensor for complex wavefront detection in adaptive optical microscopes

The Shack-Hartmann wavefront sensor (SHWS) is an essential tool for wavefront sensing in adaptive optical microscopes. However, the distorted spots induced by the complex wavefront challenge its detection performance. Here, we propose a deep learning based wavefront detection method which combines p...

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Bibliographic Details
Published in:Frontiers of information technology & electronic engineering 2021-10, Vol.22 (10), p.1277-1288
Main Authors: Hu, Shuwen, Hu, Lejia, Gong, Wei, Li, Zhenghan, Si, Ke
Format: Article
Language:English
Subjects:
Brain research
Centroids
Communications Engineering
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Deep learning
Electrical Engineering
Electronics and Microelectronics
Instrumentation
Methods
Microlenses
Networks
Optical microscopes
Optics
Point spread functions
Science
Sensors
Shack-Hartmann sensors
Stitches
Stitching
Wave front sensors
Wave fronts
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Summary:The Shack-Hartmann wavefront sensor (SHWS) is an essential tool for wavefront sensing in adaptive optical microscopes. However, the distorted spots induced by the complex wavefront challenge its detection performance. Here, we propose a deep learning based wavefront detection method which combines point spread function image based Zernike coefficient estimation and wavefront stitching. Rather than using the centroid displacements of each micro-lens, this method first estimates the Zernike coefficients of local wavefront distribution over each micro-lens and then stitches the local wavefronts for reconstruction. The proposed method can offer low root mean square wavefront errors and high accuracy for complex wavefront detection, and has potential to be applied in adaptive optical microscopes.
ISSN:2095-9184
2095-9230
DOI:10.1631/FITEE.2000422