Abstract

In polarimetric imaging, the uneven illumination could cause the significant spatial intensity fluctuations in the scene, and thus hampers the target detection. In this paper, we propose a method of illumination compensation and contrast optimization for Stokes polarimetric imaging, which allows significantly increasing the performance of target detection under uneven illumination. We show with numerical simulation and real-world experiment that, based on the intensity information contained in the polarization information, the contrast can be effectively enhanced by proper approach, which is of particular importance in practical applications with spatial illumination fluctuations, such as remote sensing.

© 2015 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
When is polarimetric imaging preferable to intensity imaging for target detection?

François Goudail and J. Scott Tyo
J. Opt. Soc. Am. A 28(1) 46-53 (2011)

Comparison of different active polarimetric imaging modes for target detection in outdoor environment

Nicolas Vannier, François Goudail, Corentin Plassart, Matthieu Boffety, Patrick Feneyrou, Luc Leviandier, Frédéric Galland, and Nicolas Bertaux
Appl. Opt. 55(11) 2881-2891 (2016)

Improving target discrimination ability of active polarization imagers by spectral broadening

Lijo Thomas, Matthieu Boffety, and François Goudail
Opt. Express 23(26) 33514-33528 (2015)

References

  • View by:
  • |
  • |
  • |

  1. L. B. Wolff, Polarization Methods in Computer Vision (Columbia University, 1991).
  2. J. S. Tyo, D. L. Goldstein, D. B. Chenault, and J. A. Shaw, “Review of passive imaging polarimetry for remote sensing applications,” Appl. Opt. 45(22), 5453–5469 (2006).
    [Crossref] [PubMed]
  3. A. Pierangelo, A. Benali, M.-R. Antonelli, T. Novikova, P. Validire, B. Gayet, and A. De Martino, “Ex-vivo characterization of human colon cancer by Mueller polarimetric imaging,” Opt. Express 19(2), 1582–1593 (2011).
    [Crossref] [PubMed]
  4. F. Meriaudeau, M. Ferraton, C. Stolz, O. Morel, and L. Bigué, “Polarization imaging for industrial inspection,” Proc. SPIE 6813, 681308 (2008).
    [Crossref]
  5. G. Anna, F. Goudail, and D. Dolfi, “Polarimetric target detection in the presence of spatially fluctuating Mueller matrices,” Opt. Lett. 36(23), 4590–4592 (2011).
    [Crossref] [PubMed]
  6. F. Goudail and A. Bénière, “Optimization of the contrast in polarimetric scalar images,” Opt. Lett. 34(9), 1471–1473 (2009).
    [Crossref] [PubMed]
  7. F. Goudail, “Optimization of the contrast in active Stokes images,” Opt. Lett. 34(2), 121–123 (2009).
    [Crossref] [PubMed]
  8. G. Anna, F. Goudail, and D. Dolfi, “Optimal discrimination of multiple regions with an active polarimetric imager,” Opt. Express 19(25), 25367–25378 (2011).
    [Crossref] [PubMed]
  9. T. Lillesand, R. W. Kiefer, and J. Chipman, Remote Sensing and Image Interpretation (John Wiley & Sons, 2014).
  10. K. Fukunaga, Introduction to Statistical Pattern Recognition (Academic, 2013).
  11. C. Rother, V. Kolmogorov, and A. Blake, ““GrabCut”-interactive foreground extraction using iterated graph cuts,” ACM Trans. Graph. 23(3), 309–314 (2004).
    [Crossref]
  12. M. Boffety, H. Hu, and F. Goudail, “Contrast optimization in broadband passive polarimetric imaging,” Opt. Lett. 39(23), 6759–6762 (2014).
    [Crossref] [PubMed]
  13. H. Hu, G. Anna, and F. Goudail, “On the performance of the physicality-constrained maximum-likelihood estimation of Stokes vector,” Appl. Opt. 52(27), 6636–6644 (2013).
    [Crossref] [PubMed]
  14. L. Goldstein Dennis, Polarized Light (Marcel Dekker, 2003).

2014 (1)

2013 (1)

2011 (3)

2009 (2)

2008 (1)

F. Meriaudeau, M. Ferraton, C. Stolz, O. Morel, and L. Bigué, “Polarization imaging for industrial inspection,” Proc. SPIE 6813, 681308 (2008).
[Crossref]

2006 (1)

2004 (1)

C. Rother, V. Kolmogorov, and A. Blake, ““GrabCut”-interactive foreground extraction using iterated graph cuts,” ACM Trans. Graph. 23(3), 309–314 (2004).
[Crossref]

Anna, G.

Antonelli, M.-R.

Benali, A.

Bénière, A.

Bigué, L.

F. Meriaudeau, M. Ferraton, C. Stolz, O. Morel, and L. Bigué, “Polarization imaging for industrial inspection,” Proc. SPIE 6813, 681308 (2008).
[Crossref]

Blake, A.

C. Rother, V. Kolmogorov, and A. Blake, ““GrabCut”-interactive foreground extraction using iterated graph cuts,” ACM Trans. Graph. 23(3), 309–314 (2004).
[Crossref]

Boffety, M.

Chenault, D. B.

De Martino, A.

Dolfi, D.

Ferraton, M.

F. Meriaudeau, M. Ferraton, C. Stolz, O. Morel, and L. Bigué, “Polarization imaging for industrial inspection,” Proc. SPIE 6813, 681308 (2008).
[Crossref]

Gayet, B.

Goldstein, D. L.

Goudail, F.

Hu, H.

Kolmogorov, V.

C. Rother, V. Kolmogorov, and A. Blake, ““GrabCut”-interactive foreground extraction using iterated graph cuts,” ACM Trans. Graph. 23(3), 309–314 (2004).
[Crossref]

Meriaudeau, F.

F. Meriaudeau, M. Ferraton, C. Stolz, O. Morel, and L. Bigué, “Polarization imaging for industrial inspection,” Proc. SPIE 6813, 681308 (2008).
[Crossref]

Morel, O.

F. Meriaudeau, M. Ferraton, C. Stolz, O. Morel, and L. Bigué, “Polarization imaging for industrial inspection,” Proc. SPIE 6813, 681308 (2008).
[Crossref]

Novikova, T.

Pierangelo, A.

Rother, C.

C. Rother, V. Kolmogorov, and A. Blake, ““GrabCut”-interactive foreground extraction using iterated graph cuts,” ACM Trans. Graph. 23(3), 309–314 (2004).
[Crossref]

Shaw, J. A.

Stolz, C.

F. Meriaudeau, M. Ferraton, C. Stolz, O. Morel, and L. Bigué, “Polarization imaging for industrial inspection,” Proc. SPIE 6813, 681308 (2008).
[Crossref]

Tyo, J. S.

Validire, P.

ACM Trans. Graph. (1)

C. Rother, V. Kolmogorov, and A. Blake, ““GrabCut”-interactive foreground extraction using iterated graph cuts,” ACM Trans. Graph. 23(3), 309–314 (2004).
[Crossref]

Appl. Opt. (2)

Opt. Express (2)

Opt. Lett. (4)

Proc. SPIE (1)

F. Meriaudeau, M. Ferraton, C. Stolz, O. Morel, and L. Bigué, “Polarization imaging for industrial inspection,” Proc. SPIE 6813, 681308 (2008).
[Crossref]

Other (4)

T. Lillesand, R. W. Kiefer, and J. Chipman, Remote Sensing and Image Interpretation (John Wiley & Sons, 2014).

K. Fukunaga, Introduction to Statistical Pattern Recognition (Academic, 2013).

L. Goldstein Dennis, Polarized Light (Marcel Dekker, 2003).

L. B. Wolff, Polarization Methods in Computer Vision (Columbia University, 1991).

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (8)

Fig. 1
Fig. 1 Intensity distribution of the illumination light on the scene.
Fig. 2
Fig. 2 (a) Measured Stokes vectors of the scene in numerical simulation. (b) Normalized Stokes vectors of the scene in numerical simulation.
Fig. 3
Fig. 3 (a) Optimal image without illumination compensation (α = 43°, ε = 6°). (b) Image with compensation illumination (α = 43°, ε = 6°). (c) Optimal image with illumination compensation (α = −41°, ε = −18°).
Fig. 4
Fig. 4 (a) Scheme of the observed scene. (b) Intensity image of the scene.
Fig. 5
Fig. 5 (a) Measured the Stokes vectors of the scene in real-world experiment. (b) Normalized Stokes vectors of the scene in real-world experiment.
Fig. 6
Fig. 6 (a) Optimal image of the scene in Fig. 3 without compensation with (α = −11°, ε = 24°). (b) Compensating the illumination by keeping the PSA state unchanged.
Fig. 7
Fig. 7 Fisher ratio as a function of azimuth and ellipticity of t without compensate the illumination (a) and with compensation (b). Both maps where created using a step of 1 degree.
Fig. 8
Fig. 8 (a) Compensating the illumination on the basis of Fig. 4(a). (b) Optimal image of the scene with illumination compensation.

Equations (12)

Equations on this page are rendered with MathJax. Learn more.

S u = M u S n ,
i u = 1 2 T T S u ,
i u = 1 2 T T S u + n ,
( T ) = [ i a i b ] 2 var [ i a ] + var [ i b ] ,
i u = 1 2 T T S u ,
var [ i u ] = 1 4 T T ( S u S u ) ( S u S u ) T T + σ 2 ,
var [ i u ] = 1 4 T T ( S u S u ) ( S u S u ) T T + σ 2 = 1 4 ( [ Δ S 0 u ] 2 + t T Δ s u Δ s u T t + Δ S 0 u Δ s u T t + t T Δ s u Δ S 0 u ) + σ 2 ,
i u ' = i u S 0 u S 0 u = 1 2 T T ( S u S 0 u ) S 0 u + n ' ,
var [ n ' ] = var [ n S 0 u S 0 u ] = var [ n S 0 u ] S 0 u 2 = σ ' 2 .
var [ i u ' ] = 1 4 T T [ ( S u S 0 u ) S 0 u ( S u S 0 u ) S 0 u ] [ ( S u S 0 u ) S 0 u ( S u S 0 u ) S 0 u ] T T + σ ' 2 = 1 4 [ 1 , t T ] [ [ 1 s u / S 0 u ] S 0 u [ 1 s u / S 0 u ] S 0 u ] [ [ 1 s u / S 0 u ] S 0 u [ 1 s u / S 0 u ] S 0 u ] T [ 1 t ] + σ ' 2 = 1 4 t T ( s u S 0 u S 0 u s u S 0 u S 0 u ) ( s u S 0 u S 0 u s u S 0 u S 0 u ) T t + σ ' 2 .
( T ) = [ i a ' i b ' ] 2 var [ i a ' ] + var [ i b ' ] .
S a = [ 1 0.09 0.62 0.08 ] , S b = [ 1 0.10 0.45 0.21 ] .

Metrics