A Class-Adaptive Spatially Variant Mixture Model for Image Segmentation
IEEE Transactions on Image Processing
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We propose a new approach for the restoration of polarimetric Stokes images, capable of simultaneously segmenting and restoring the images. In order to easily handle the admissibility constraints inherent to Stokes images, a proper transformation of the images is introduced. This transformation exploits the correspondence between any Stokes vector and the covariance matrix of the two components of the electric vector of the light wave. A Bayesian model based on a mixture of Gaussian kernels is used for the transformed images. Inference is achieved using the EM framework. To quantify the performances of this approach, the algorithm is tested with both synthetic and real data. We note that the pixels of the restored Stokes images issued from our approach are always physically admissible which is not the case for the naïve pseudo-inverse approach.