Many of the despeckling filters currently available (Mean, Median, Lee
Sigma, Lee MMSE, Kuan, Frost) operate by smoothing over a fixed
window, whose size must be decided by two competing factors. Over
homogeneous regions large window sizes are needed to improve speckle
reduction by averaging. However, a large window size reduces the fundamental
resolution of the algorithm, as with multi-looking.
For instance, when one of these filters attempts to reconstruct a small
bright object, it produces artifacts around the object over a distance equal
to the filter dimension. This means that the background is badly defined in
the neighbourhood of bright targets and edges, which is just where one would
like it to be accurate.
In this paper, these problems are overcome by introducing a correlated
neighbourhood model into the MAP filter. This filter operates on a small
window and so is able to preserve resolution. The correlation model allows
us to describe both the scene heterogeneity and the effects of partial
smoothing, which in turn, allows us to iterate the filter, hence, increasing
the amount of smoothing that can be achieved with a small window.
This gives a filter that is able to adapt to the underlying fluctuations
of the scene, preserve detail and still achieve large amounts of smoothing.
The final iterated filter is then compared with the current DERA simulated
annealing algorithm.
