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 Mosk

doi:10.1117/12.3029461

18 June 2024 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 Mosk

Author Affiliations +

Proceedings Volume PC13023, Computational Optics 2024; PC130230H (2024) https://doi.org/10.1117/12.3029461
Event: SPIE Optical Systems Design, 2024, Strasbourg, France

Abstract

We introduce a novel method for maximum-likelihood estimation in ptychography to address the challenge posed by mixed Poisson-Gaussian noise statistics. By integrating a loss function that accounts for both noise sources in computational image retrieval, our approach significantly improves image reconstruction quality under low signal-to-noise ratio conditions. Experimental and numerical data confirm the advantage of our method over traditional approaches that consider only Poissonian noise. This advancement promises enhanced performance in computational imaging applications, particularly in situations where accurate noise modeling is crucial.

Conference Presentation

Citation Download Citation

Jacob Seifert, Yifeng Shao, Rens van Dam, Dorian Bouchet, Tristan van Leeuwen, and Allard Mosk "Maximum-likelihood estimation in ptychography in the presence of Poisson-Gaussian noise statistics", Proc. SPIE PC13023, Computational Optics 2024, PC130230H (18 June 2024); https://doi.org/10.1117/12.3029461

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