Light microscopy imaging usually suffers of a limited
depth of field. That is the region in front and
behind the object focal plane that will be in acceptable focus. Most of the times, the specimen
depth is much larger that the depth of field. Thus,
in order to study the entire specimen we have to
move it along the optical axis, obtaining several
images with different regions appearing in focus
on each of them. Extended depth of field (EDF)
is a fusion algorithm that from the obtained stack
of images produces one assembled image containing
all the in focus regions. In this way we obtain
one in focus image as if the depth of field of
the optical system was “extended”. There are different
EDF algorithms among which we can mention
those based on the discrete wavelet transform;
these giving the most promising results. However,
these algorithms tend to produce some artifacts
on the resulting images and require some heuristic
processing to give acceptable results.
In this project we propose a new EDF algorithm
that avoids this empirical approach by using
hidden Markov models (HMM) in wavelet domain [1].
We use HMM to model depencies between
wavelet coefficients.
We obtain a
very rich model of the wavelet coefficients, that can be used to estimate the topology of the
specimen.
