  Cornea cell count is an important diagnostic tool commonly used by
practitioners to assess the health of a patient's cornea. Unfortunately,
clinical specular microscopy requires the acquisition of a large number of
images at different focus depths because the curved shape of the cornea makes
it impossible to acquire a single all-in-focus image. This paper describes two
methods and their implementations to reduce the number of images required to
run a cell-counting algorithm, thus shortening the duration of the examination
and increasing the patient's comfort. The basic idea is to apply de-blurring
techniques on the raw images to reconstruct the out-of-focus areas and expand
the sharp regions of the image. Our approach is based on blind-deconvolution
reconstruction that performs a depth-from-deblur so to either model Gaussian
kernel or to fit kernels from an ad hoc lookup table.
