  In a particle beam microscope, a raster-scanned focused beam of particles
interacts with a sample to generate a secondary electron (SE) signal pixel by
pixel. Conventionally formed micrographs are noisy because of limitations on
acquisition time and dose. Recent work has shown that estimation methods
applicable to a time-resolved measurement paradigm can greatly reduce noise,
but these methods apply pixel by pixel without exploiting image structure. Raw
SE count data can be modeled with a compound Poisson (Neyman Type A)
likelihood, which implies data variance that is signal-dependent and greater
than the variation in the underlying particle-sample interaction. These
statistical properties make methods that assume additive white Gaussian noise
ineffective. This paper introduces methods for particle beam micrograph
denoising that use the plug-and-play framework to exploit image structure while
being applicable to the unusual data likelihoods of this modality.
Approximations of the data likelihood that vary in accuracy and computational
complexity are combined with denoising by total variation regularization, BM3D,
and DnCNN. Methods are provided for both conventional and time-resolved
measurements, assuming SE counts are available. In simulations representative
of helium ion microscopy and scanning electron microscopy, significant
improvements in root mean-squared error (RMSE), structural similarity index
measure (SSIM), and qualitative appearance are obtained. Average reductions in
RMSE are by factors ranging from 2.24 to 4.11.
