  Skymaps measured with imaging atmospheric Cherenkov telescopes (IACTs)
represent the real source distribution convolved with the point spread function
of the observing instrument. Current IACTs have an angular resolution in the
order of 0.1$^\circ$ which is rather large for the study of morphological
structures and for comparing the morphology in $\gamma$-rays to measurements in
other wavelengths where the instruments have better angular resolutions.
  Serendipitously it is possible to approximate the underlying true source
distribution by applying a deconvolution algorithm to the observed skymap, thus
effectively improving the instruments angular resolution. From the multitude of
existing deconvolution algorithms several are already used in astronomy, but in
the special case of $\gamma$-ray astronomy most of these algorithms are
challenged due to the high noise level within the measured data.
  One promising algorithm for the application to $\gamma$-ray data is the
Maximum Entropy Algorithm. The advantages of this algorithm are the possibility
to take a priori knowledge into account and that it is an independent approach
to previous work, e.g., Heinz et al. (2012) who applied the Richardson Lucy
Algorithm to $\gamma$-ray skymaps.
  An implementation of the Maximum Entropy Algorithm is provided in the MemSys5
software package by Gull and Skilling (1999). As this algorithm is very
sensitive to various input parameters it is essential to understand their
influences. We present a study of the influences of these parameters in order
to investigate the applicability of the Maximum Entropy Algorithm for the
deconvolution of skymaps in $\gamma$-ray astronomy.
