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
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.