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.

Optics Letters
Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands

Seifert, J., Shao, Y., van Dam, R., Bouchet, D., van Leeuwen, T., & Mosk, A. (2023). Maximum-likelihood estimation in ptychography in the presence of Poisson–Gaussian noise statistics. Optics Letters, 48(22), 6027–6030. doi:10.1364/OL.502344