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Published on: December 15, 2017

Estimation of physically realizable Mueller matrices from experiments using global constrained optimization.

Jawad Elsayed Ahmad1, Yoshitate Takakura

  • 1Université Louis Pasteur, Strasbourg I Laboratoire des Sciences de l'Image et de la Télédétection CNRS UMR 7005, Boulevard Sébastien Brant, 67412 Illkirch, France. jawadsayed@termxjy.u-strasbg.fr

Optics Express
|September 6, 2008
PubMed
Summary
This summary is machine-generated.

Physically realizable Mueller matrices can be retrieved from noisy intensity data using global optimization. Constrained simulated annealing offers higher accuracy than sequential quadratic programming for this purpose.

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Published on: December 15, 2017

Area of Science:

  • Optical physics
  • Polarimetry
  • Data analysis

Background:

  • Mueller matrices are essential for characterizing the polarization properties of optical systems.
  • Quantified intensity data often contains noise, complicating the retrieval of accurate Mueller matrices.
  • Ensuring physical realizability of retrieved Mueller matrices is crucial for their valid interpretation.

Purpose of the Study:

  • To develop and compare methods for retrieving physically realizable Mueller matrices from noisy intensity data.
  • To integrate physical realizability criteria into a global optimization framework.
  • To analyze the robustness and accuracy of different optimization techniques.

Main Methods:

  • Implementing a global optimization process incorporating the physical realizability criterion.
  • Testing a deterministic approach using sequential quadratic programming.
  • Testing a stochastic approach using constrained simulated annealing algorithms.

Main Results:

  • Both sequential quadratic programming and constrained simulated annealing successfully retrieved physically realizable Mueller matrices.
  • Constrained simulated annealing demonstrated higher accuracy compared to sequential quadratic programming.
  • Both methods showed robustness when applied to experimental data and an inadmissible Mueller matrix.

Conclusions:

  • Global optimization with physical realizability constraints is effective for accurate Mueller matrix retrieval.
  • Constrained simulated annealing is a robust and accurate method for this application.
  • The developed methods enable reliable characterization of polarization properties from experimental data.