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arXiv · 2308.02436

Maximum-likelihood estimation in ptychography in the presence of Poisson-Gaussian noise statistics

Jacob Seifert·Yifeng Shao·Rens van Dam·Dorian Bouchet·Tristan van Leeuwen·Allard P. Mosk
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Abstract

Optical measurements often exhibit mixed Poisson-Gaussian noise statistics, which hampers image quality, particularly under low signal-to-noise ratio (SNR) conditions. Computational imaging falls short in such situations when solely Poissonian noise statistics are assumed. In response to this challenge, we define a loss function that explicitly incorporates this mixed noise nature. By using maximum-likelihood estimation, we devise a practical method to account for camera readout noise in gradient-based ptychography optimization. Our results, based on both experimental and numerical data, demonstrate that this approach outperforms the conventional one, enabling enhanced image reconstruction quality under challenging noise conditions through a straightforward methodological adjustment.

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Jacob Seifert, Yifeng Shao, Rens van Dam, Dorian Bouchet, Tristan van Leeuwen, Allard P. Mosk. 2023-10-11. Maximum-likelihood estimation in ptychography in the presence of Poisson-Gaussian noise statistics. https://doi.org/10.1364/ol.502344

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