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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 solelyPoissonian noise statistics are assumed. In response to this challenge, wedefine a loss function that explicitly incorporates this mixed noise nature. Byusing maximum-likelihood estimation, we devise a practical method to accountfor camera readout noise in gradient-based ptychography optimization. Ourresults, based on both experimental and numerical data, demonstrate that thisapproach outperforms the conventional one, enabling enhanced imagereconstruction quality under challenging noise conditions through astraightforward methodological adjustment.

Maximum-likelihood estimation in ptychography in the presence of Poisson-Gaussian noise statistics