摘要：We have compared the performance of two different penalty choices
for a penalized-likelihood sinogram-restoration strategy we have
been developing. One is a quadratic penalty we have employed
previously and the other is a new median-based penalty. We
compared the approaches to a noniterative adaptive filter that
loosely but not explicitly models data statistics. We found that
the two approaches produced similar resolution-variance tradeoffs
to each other and that they outperformed the adaptive filter in
the low-dose regime, which suggests that the particular choice of
penalty in our approach may be less important than the fact that
we are explicitly modeling data statistics at all. Since the
quadratic penalty allows for derivation of an algorithm that is
guaranteed to monotonically increase the penalized-likelihood
objective function, we find it to be preferable to the median-based penalty.
