  We propose a new microscopy simulation system that can depict atomistic
models in a micrograph visual style, similar to results of physical electron
microscopy imaging. This system is scalable, able to represent simulation of
electron microscopy of tens of viral particles and synthesizes the image faster
than previous methods. On top of that, the simulator is differentiable, both
its deterministic as well as stochastic stages that form signal and noise
representations in the micrograph. This notable property has the capability for
solving inverse problems by means of optimization and thus allows for
generation of microscopy simulations using the parameter settings estimated
from real data. We demonstrate this learning capability through two
applications: (1) estimating the parameters of the modulation transfer function
defining the detector properties of the simulated and real micrographs, and (2)
denoising the real data based on parameters trained from the simulated
examples. While current simulators do not support any parameter estimation due
to their forward design, we show that the results obtained using estimated
parameters are very similar to the results of real micrographs. Additionally,
we evaluate the denoising capabilities of our approach and show that the
results showed an improvement over state-of-the-art methods. Denoised
micrographs exhibit less noise in the tilt-series tomography reconstructions,
ultimately reducing the visual dominance of noise in direct volume rendering of
microscopy tomograms.
