Polarimetric data reduction: a Bayesian approach

Zallat, Jihad; Heinrich, Christian

doi:10.1364/oe.15.000083

Cited by 18 publications

(16 citation statements)

References 14 publications

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“…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].…”

Section: Introductionmentioning

confidence: 85%

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

Hu¹,

Anna²,

Goudail³

2013

Appl. Opt.

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We address the estimation of the Stokes vectors taking into account the physical realizability constraint. We propose a fast method for computing the constrained maximum-likelihood (CML) estimator for any measurement matrix, and we compare its performance with the classical empirical physicality-constrained estimator. We show that when the measurement matrix is based on four polarization states spanning a regular tetrahedron on the Poincaré sphere, the two estimators are very similar, but the CML provides a better estimation of the intensity. For an arbitrary measurement matrix, the CML estimator does not always yield better estimation performance than the empirical one: their comparative performances depend on the measurement matrix, the actual Stokes vector and the signal-to-noise ratio.

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Section: Introductionmentioning

confidence: 85%

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

Hu¹,

Anna²,

Goudail³

2013

Appl. Opt.

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“…Of the imaging polarimetry denoising papers, only Valenzuela and Fessler [24] used real polarization images with simulated noise. Sfikas et al, Zallat & Heinrich, and Faisan et al [16,23,25] used simple synthetic images, consisting of simple geometric shapes with regions of uniform, or smoothly varying, Stokes components. Such images are easy to denoise using basic uniform or smoothly varying regions, and as such they don't test the algorithms' ability to denoise natural images.…”

Section: B Test Imagerymentioning

confidence: 99%

Denoising imaging polarimetry by adapted BM3D method

Tibbs

Daly²,

Roberts³

et al. 2018

J. Opt. Soc. Am. A

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In addition to the visual information contained in intensity and color, imaging polarimetry allows visual information to be extracted from the polarization of light. However, a major challenge of imaging polarimetry is image degradation due to noise. This paper investigates the mitigation of noise through denoising algorithms and compares existing denoising algorithms with a new method, based on BM3D (Block Matching 3D). This algorithm, Polarization-BM3D (PBM3D), gives visual quality superior to the state of the art across all images and noise standard deviations tested. We show that denoising polarization images using PBM3D allows the degree of polarization to be more accurately calculated by comparing it with spectral polarimetry measurements.

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“…Systematic calibration errors as well as CCD noises are accounted for in the same way as in [2,3], which leads to the following observation model:…”

Section: Problem Statementmentioning

confidence: 99%

“…The Bayesian framework yields neat solutions to the polarimetric data reduction problem for the case of piecewise constant signatures [3,4]. The case of smoothly varying signatures has also been addressed, for example by Valenzuela and Fessler [5] and by Sfikas et al [6].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2012

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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.

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Polarimetric data reduction: a Bayesian approach

Cited by 18 publications

References 14 publications

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

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

Denoising imaging polarimetry by adapted BM3D method

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

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