Phase retrieval from overexposed PSF: a projection-based approach

Oleg Soloviev; Jacques Noom; Hieu Thao Nguyen; Gleb Vdovin; Michel Verhaegen

doi:10.1117/12.2609697

2 March 2022 Phase retrieval from overexposed PSF: a projection-based approach

Oleg Soloviev, Jacques Noom, Hieu Thao Nguyen, Gleb Vdovin, Michel Verhaegen

Proceedings Volume 11970, Quantitative Phase Imaging VIII; 119700K (2022) https://doi.org/10.1117/12.2609697
Event: SPIE BiOS, 2022, San Francisco, California, United States

Conference Poster

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.

Citation Download Citation

Oleg Soloviev, Jacques Noom, Hieu Thao Nguyen, Gleb Vdovin, and Michel Verhaegen "Phase retrieval from overexposed PSF: a projection-based approach", Proc. SPIE 11970, Quantitative Phase Imaging VIII, 119700K (2 March 2022); https://doi.org/10.1117/12.2609697

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