A Bayesian approach for polarimetric data reduction: the Mueller imaging case.
In this paper, we extend to the Mueller imaging framework a formerly introduced Bayesian approach dealing with polarimetric data reduction and robust clustering of polarization encoded images in the piecewise constant case. The extension was made possible thanks to a suitable writing of the observation model in the Mueller context that relies on the system's coherency matrix and Cholesky decomposition such that the admissibility constraints are easily captured. This generalization comes at the cost of nonlinearity with respect to the parameters that have to be estimated. This estimation-clustering problem is tackled in a Bayesian framework where a hierarchical stochastic model based on a Markov random field proposed by Potts is used. This fully unsupervised approach is extensively tested over synthetic data as well as real Mueller images.
Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection LSIIT, UMR CNRS-ULP 7005, Parc d'Innovation, Bd Sébastien Brant, B.P. 10413, F-67412 Illkirch cedex, France. zallat@lsiit.u-strasbg.fr
Jihad
J
Zallat
Christian
C
Heinrich
Matthieu
M
Petremand
D000465:Algorithms; D001499:Bayes Theorem; D016000:Cluster Analysis; D003198:Computer Simulation; D003952:Diagnostic Imaging / Q000379:methods; D008390:Markov Chains; D008859:Microscopy, Polarization / Q000379:methods*; D015233:Models, Statistical; D055095:Optics and Photonics; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D013269:Stochastic Processes