Phase retrieval from overexposed PSF

A projection-based approach

Conference Paper (2022)
Author(s)

OA Soloviev (Flexible Optical B.V., TU Delft - Team Michel Verhaegen)

J. Noom (TU Delft - Team Michel Verhaegen)

Hieu Thao Nguyen (TU Delft - Team Michel Verhaegen)

Gleb Vdovine (Flexible Optical B.V., TU Delft - Team Mulders)

MHG Verhaegen (TU Delft - Team Michel Verhaegen)

Research Group
Team Michel Verhaegen
Copyright
© 2022 O.A. Soloviev, J. Noom, Hieu Thao Nguyen, Gleb Vdovin, M.H.G. Verhaegen
DOI related publication
https://doi.org/10.1117/12.2609697
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 O.A. Soloviev, J. Noom, Hieu Thao Nguyen, Gleb Vdovin, M.H.G. Verhaegen
Research Group
Team Michel Verhaegen
ISBN (electronic)
9781510648111
Reuse Rights

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Abstract

We investigate the general adjustment of projection-based phase retrieval algorithms for use with saturated data. In the phase retrieval problem, model fidelity of experimental data containing a non-zero background level, fixed pattern noise, or overexposure, often presents a serious obstacle for standard algorithms. Recently, it was shown that overexposure can help to increase the signal-to-noise ratio in AI applications. We present our first results in exploring this direction in the phase retrieval problem, using as an example the Gerchberg-Saxton algorithm with simulated data. The proposed method can find application in microscopy, characterisation of precise optical instruments, and machine vision applications of Industry4.0.

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