Joint filtering estimation of Stokes vector images based on a nonlocal means approach

Abstract

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.

Denoising imaging polarimetry by adapted BM3D method

“…Our contribution is the introduction of a novel denoising algorithm, 'PBM3D', that when compared to state of the art polarization denoising algorithms, gives superior performance. When applied to a selection of noisy images, those denoised using PBM3D had a PSNR of 4.50dB greater on average than those denoised using the method of Faisan et al [16], and 0.84dB greater than those denoised using BM3D Stokes. PBM3D relies on a transformation from camera components into intensity-polarization components.…”

“…In this paper, our PBM3D algorithm will be compared to the above two algorithms. Faisan et al [16] compared their denoising algorithm with earlier methods [23][24][25] and demonstrated that their NLM based algorithm gives superior denoising performance. For this reason, comparison to these algorithms is not considered.…”

“…• Zhao: Zhao et al's method [15] • Faisan: Faisan et al's method [16] In order to quantitatively compare the denoising performance PSNR was computed for each denoised image. For Stokes image (S 0 , S 1 , S 2 ) with ground truth given by…”

On the performance of the physicality-constrained maximum-likelihood estimation of Stokes vector

“…If the intensity measurements are perturbed by noise, so is the estimated Stokes vector, and this noise may even make this estimate nonphysical, that is, its degree of polarization (DOP) may be larger than 1. Taking into account the constraint that the estimate must be physically realizable is known to improve the quality of estimation for the Stokes vector [4][5][6][7][8]. The improvement of the estimation quality obtained by taking into account the physical realizability constraint is also found to be valid for the Mueller matrix [5,[9][10][11].…”

“…In this paper, we assume that Stokes measurements are perturbed by additive Gaussian noise, and we consider a constrained maximum-likelihood (CML) estimator [8] to perform the estimation. Using the Lagrange multiplier method, we first propose a fast, noniterative method to determine the CML estimator for any Stokes measurement matrix.…”