Phase retrieval from overexposed PSF
A projection-based approach
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)
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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.