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In the framework of Stokes parameters imaging, pola rization- encoded images have four channels. The potential of such multidimensional structure comes from the set of ph ysical information they carry about the local nature of th e target. However, the noise that affects the intensity measur ement may induce the non physical meaning of Stokes parameter s making awkward their analysis and interpretation. This ind uces the need for a proper tool that allows the processing of pola rization- encoded images while respecting their physical cont ent. In this paper a new method to filter the additive noise of polarimetric measurement is addressed. This method is based on t wo multispectral filtering methods combined with a tran sformation of Stokes channels following a fractal path. The propos ed algorithm is a tradeoff between the filtering of polarization- encoded images and the preserving of their physical content. The s tatistical performances of the method are tested on simulated and real images using Bootstrap re-sampling. reached so as to minimize the effect of the noise a ffecting polarimetric images and to preserve their physical meaning. In this paper, images being acquired under incohere nt illumination, additive noise is estimated and eliminated such tha t the physical content of the polarimetric images is preserved as much as possible. The estimation methods are chosen in the range of multispectral filtering methods. In order to take a dvantage of both methods, the filtering algorithm deals with a combi nation of Scatter plot (6) and data masking methods (4). As the infor mation content of polarization-encoded images is intricately combi ned in several polarization channels, Peano-Hilbert fractal path i s applied on the noisy image to keep the connexity of homogeneous areas and to minimize the impact of the outliers. The performanc es and the bias of the method are statistically investigated by Boo tstrap method (7).

Noise Estimation of Polarization-Encoded Images by Peano-Hilbert Fractal Path