Maximum-likelihood estimation in ptychography in the presence of Poisson–Gaussian noise statistics
Jacob Seifert (Universiteit Utrecht)
Yifeng Shao (TU Delft - ImPhys/Coene group, Universiteit Utrecht)
Rens van Dam (Universiteit Utrecht, Student TU Delft)
Dorian Bouchet (Université Grenoble Alpes)
Tristan van Leeuwen (Universiteit Utrecht, Centrum Wiskunde & Informatica (CWI))
Allard P. Mosk (Universiteit Utrecht)
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Abstract
Optical measurements often exhibit mixed Poisson–Gaussian noise statistics, which hampers the 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 a maximum-likelihood estimation, we devise a practical method to account for a 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.