Maximum-likelihood estimation in ptychography in
the presence of Poisson–aussian noise statistics
Posted on 2023-11-16 - 19:50
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.
CITE THIS COLLECTION
Seifert, Jacob; Shao, Yifeng; van Dam, Rens; Bouchet, Dorian; van Leeuwen, Tristan; Mosk, Allard (2023). Maximum-likelihood estimation in ptychography in the presence of Poisson–aussian noise statistics. Optica Publishing Group. Collection. https://doi.org/10.6084/m9.figshare.c.6893302.v3
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Rens van Dam
Tristan van Leeuwen