  Microscopy is one of the most essential imaging techniques in life sciences.
High-quality images are required in order to solve (potentially life-saving)
biomedical research problems. Many microscopy techniques do not achieve
sufficient resolution for these purposes, being limited by physical diffraction
and hardware deficiencies. Electron microscopy addresses optical diffraction by
measuring emitted or transmitted electrons instead of photons, yielding
nanometer resolution. Despite pushing back the diffraction limit, blur should
still be taken into account because of practical hardware imperfections and
remaining electron diffraction. Deconvolution algorithms can remove some of the
blur in post-processing but they depend on knowledge of the point-spread
function (PSF) and should accurately regularize noise. Any errors in the
estimated PSF or noise model will reduce their effectiveness. This paper
proposes a new procedure to estimate the lateral component of the point spread
function of a 3D scanning electron microscope more accurately. We also propose
a Bayesian maximum a posteriori deconvolution algorithm with a non-local image
prior which employs this PSF estimate and previously developed noise
statistics. We demonstrate visual quality improvements and show that applying
our method improves the quality of subsequent segmentation steps.
