The design of an optimal Mueller imaging polarimeter is not obvious: it necessitates the use of a well-conditioned polarization state generator as well as a well-conditioned polarization state analyser that permits proper inversion of intensity images to produce the Mueller matrix images. Even though a rigorous calibration procedure is applied, there remain uncertainties on measurements due to the non-ideal optical elements, misalignment, and photon noise in CCD cameras. The relationship between system conditioning and signal-to-noise ratio in an imaging polarimeter has been largely addressed in the recent literature. Herein, a different approach is used to study the error propagation and the impact of noise on the measurements in such a calibrated system quantitatively. The main contributions of this paper are: an extended theory of noise and errors, a development of the effect of noise resulting in a different merit function, a novel polarimeter solution using two retarders, and a practical demonstration of the sensitivity to noise, which are of interest for polarimetric imaging system designers.

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.

Conventional estimation techniques of Stokes images from observed radiance images through different polarization filters suffer from noise contamination that hampers correct interpretation or even leads to unphysical estimated signatures. This paper presents an efficient restoration technique based on nonlocal means, permitting accurate estimation of smoothly variable polarization signatures in the Stokes image while preserving sharp transitions. The method is assessed on simulated data as well as on real images.

In this paper, we introduce a general Bayesian approach to estimate polarization parameters in the Stokes imaging framework. We demonstrate that this new approach yields a neat solution to the polarimetric data reduction problem that preserves the physical admissibility constraints and provides a robust clustering of Stokes images in regard to image noises. The proposed approach is extensively evaluated by using synthetic simulated data and applied to cluster and retrieves the Stokes image issuing from a set of real measurements.