Cancellation of motion artifacts caused by a division-of-time polarimeter

For any kind of imaging polarimeter, at least four intensity images, named polarization state images, are needed to compute one full Stokes vector. When the polarimeter is designed according to the division-of-time principle, polarization state images are acquired sequentially. Consequently, the main issue is the systematic occurrence of artifacts as the scene is not static. Even though this is well known, little research has been done on this subject. Here a two-step motion-compensation-based method is proposed to fix it. The first step consists in estimating the motion between each image acquired according to the same polarization state. Then each image is warped according to a fraction of the previously estimated motion. Due to their dense and accurately estimated motion field we have shown optical-flow techniques to be the most efficient for motion-estimation in this case. Compensating the motion using optical flow to estimate it actually leads to a strong correlation criterion between corrected and reference polarization images. Our method allows the estimation of the polarization by post-processing the polarization state image sequence. It leads to a good estimation quality whether the scene is static or not, thus fixing the main issue of a divisionof- time polarimeter.

[1]  Louis A. Hageman,et al.  Iterative Solution of Large Linear Systems. , 1971 .

[2]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[3]  A. de Martino,et al.  Registration scheme suitable to Mueller matrix imaging for biomedical applications. , 2007, Optics express.

[4]  Andreas G. Andreou,et al.  Liquid crystal polarization camera , 1997, IEEE Trans. Robotics Autom..

[5]  Richard Szeliski,et al.  A Database and Evaluation Methodology for Optical Flow , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[6]  Laurent Bigué,et al.  High-speed portable polarimeter using a ferroelectric liquid crystal modulator , 2007, SPIE Optical Engineering + Applications.

[7]  Laurent Bigué,et al.  High-Speed Acquisition and Pre-processing of Polarimetric Image Sequences , 2008, ACIVS.

[8]  L. W. Chubb,et al.  Polarized Light , 2019, Light Science.

[9]  D Marr,et al.  Directional selectivity and its use in early visual processing , 1981, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[10]  J Scott Tyo,et al.  Review of passive imaging polarimetry for remote sensing applications. , 2006, Applied optics.

[11]  Patrick Pérez,et al.  A multigrid approach for hierarchical motion estimation , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[12]  Thomas Brox,et al.  Variational Motion Segmentation with Level Sets , 2006, ECCV.

[13]  Thomas Brox,et al.  High Accuracy Optical Flow Estimation Based on a Theory for Warping , 2004, ECCV.

[14]  David B. Chenault,et al.  Automated registration of polarimetric imagery using Fourier transform techniques , 2002, SPIE Optics + Photonics.