A Physically Admissible Stokes Vector Reconstruction in Linear Polarimetric Imaging

Guyader, Carole Le; Aïnouz, Samia; Canu, Stéphane

doi:10.1007/s10851-022-01139-2

Cited by 1 publication

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“…Several methods for the recovery of Stokes images from intensity measurements have been proposed. These include Bayesian approaches [5,6], non-local means [7], regularized [8] and min-max optimization [9]. In contrast, this paper exploits the natural representation of a Stokes image as a third-order tensor to leverage the powerful framework of multilinear algebra, in particular, low-rank tensor decompositions [10].…”

Section: Introductionmentioning

confidence: 99%

Physically-Constrained Block-Term Tensor Decomposition for Polarimetric Image Recovery

Barreto,

Flamant,

Miron

et al. 2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper introduces a complete approach for the recovery of polarimetric images from experimental intensity measurements. In many applications, such images collect, at each pixel, a Stokes vector encoding the polarization state of light. By representing a Stokes vector image as a third-order tensor, we propose a new physicallyconstrained block-term tensor decomposition called Stokes-BTD. The proposed model is flexible and comes with broad identifiability guarantees. Moreover, physical constraints ensure meaningful interpretation of low-rank terms as Stokes vectors. In practice, Stokes images must be recovered from indirect, intensity measurements. To this aim, we implement two recovery algorithms for Stokes-BTD based on constrained alternated optimization and highlight constraints related to Stokes vectors. Numerical experiments on synthetic and real data illustrate the potential of the approach.

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