Abstract

Light scattering due to interaction with a material has long been known to create speckle patterns. We have demonstrated that even though speckle patterns from different objects are very similar, they contain minute dissimilarities that can be used to differentiate between the originating scatterers. We first approached this problem using a convolutional neural network—a deep learning algorithm—to show that indeed specific speckle patterns can be linked to the respective materials creating them. We then progressed to use recorded speckle patterns created from different materials in order to measure statistical parameters that possess a well-defined physical meaning. Using these parameters gave similar scatterer recognition abilities while gaining insight on the physical reasons for these material-dependent statistical deviations.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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  1. J. W. Goodman, Speckle Phenomena in Optics: Theory and Applications (Roberts & Company, 2007).
  2. C. Dainty, E. Ennos, M. Francon, J. Goodman, S. McKenchnie, and G. Parry, Laser Speckle and Related Phenomena (Springer, 1975).
  3. J. S. Lee, L. Jurkevich, P. Dewaele, P. Wambacq, and A. Oosterlinck, Remote Sens. Rev. 8, 313 (1994).
    [Crossref]
  4. R. Erf, Speckle Metrology (Elsevier, 2012).
  5. O. Katz, P. Heidmann, M. Fink, and S. Gigan, Nat. Photonics 8, 784 (2014).
    [Crossref]
  6. J. Leendertz, J. Phys. E 3, 214 (1970).
    [Crossref]
  7. G. Danuser and C. Waterman-Storer, Annu. Rev. Biophys. Biomol. Struct. 35, 361 (2006).
    [Crossref]
  8. W. B. Ribbens, Appl. Opt. 8, 2173 (1969).
    [Crossref]
  9. F. Luk, V. Huynh, and W. North, J. Phys. E 22, 977 (1989).
    [Crossref]
  10. M. Egmont-Petersena, D. Ridderb, and H. Handelsc, Pattern Recognit. 35, 2279 (2002).
    [Crossref]
  11. G. McLachlan, Discriminant Analysis and Statistical Pattern Recognition (Wiley, 2004).
  12. CNN to predict materials from their corresponding speckles, https://doi.org/10.6084/m9.figshare.5777598 .
  13. Parameter vectors from speckle images, https://doi.org/10.6084/m9.figshare.5777595 .
  14. LDA to predict materials from their corresponding parameter vectors, https://doi.org/10.6084/m9.figshare.5777601 .

2014 (1)

O. Katz, P. Heidmann, M. Fink, and S. Gigan, Nat. Photonics 8, 784 (2014).
[Crossref]

2006 (1)

G. Danuser and C. Waterman-Storer, Annu. Rev. Biophys. Biomol. Struct. 35, 361 (2006).
[Crossref]

2002 (1)

M. Egmont-Petersena, D. Ridderb, and H. Handelsc, Pattern Recognit. 35, 2279 (2002).
[Crossref]

1994 (1)

J. S. Lee, L. Jurkevich, P. Dewaele, P. Wambacq, and A. Oosterlinck, Remote Sens. Rev. 8, 313 (1994).
[Crossref]

1989 (1)

F. Luk, V. Huynh, and W. North, J. Phys. E 22, 977 (1989).
[Crossref]

1970 (1)

J. Leendertz, J. Phys. E 3, 214 (1970).
[Crossref]

1969 (1)

Dainty, C.

C. Dainty, E. Ennos, M. Francon, J. Goodman, S. McKenchnie, and G. Parry, Laser Speckle and Related Phenomena (Springer, 1975).

Danuser, G.

G. Danuser and C. Waterman-Storer, Annu. Rev. Biophys. Biomol. Struct. 35, 361 (2006).
[Crossref]

Dewaele, P.

J. S. Lee, L. Jurkevich, P. Dewaele, P. Wambacq, and A. Oosterlinck, Remote Sens. Rev. 8, 313 (1994).
[Crossref]

Egmont-Petersena, M.

M. Egmont-Petersena, D. Ridderb, and H. Handelsc, Pattern Recognit. 35, 2279 (2002).
[Crossref]

Ennos, E.

C. Dainty, E. Ennos, M. Francon, J. Goodman, S. McKenchnie, and G. Parry, Laser Speckle and Related Phenomena (Springer, 1975).

Erf, R.

R. Erf, Speckle Metrology (Elsevier, 2012).

Fink, M.

O. Katz, P. Heidmann, M. Fink, and S. Gigan, Nat. Photonics 8, 784 (2014).
[Crossref]

Francon, M.

C. Dainty, E. Ennos, M. Francon, J. Goodman, S. McKenchnie, and G. Parry, Laser Speckle and Related Phenomena (Springer, 1975).

Gigan, S.

O. Katz, P. Heidmann, M. Fink, and S. Gigan, Nat. Photonics 8, 784 (2014).
[Crossref]

Goodman, J.

C. Dainty, E. Ennos, M. Francon, J. Goodman, S. McKenchnie, and G. Parry, Laser Speckle and Related Phenomena (Springer, 1975).

Goodman, J. W.

J. W. Goodman, Speckle Phenomena in Optics: Theory and Applications (Roberts & Company, 2007).

Handelsc, H.

M. Egmont-Petersena, D. Ridderb, and H. Handelsc, Pattern Recognit. 35, 2279 (2002).
[Crossref]

Heidmann, P.

O. Katz, P. Heidmann, M. Fink, and S. Gigan, Nat. Photonics 8, 784 (2014).
[Crossref]

Huynh, V.

F. Luk, V. Huynh, and W. North, J. Phys. E 22, 977 (1989).
[Crossref]

Jurkevich, L.

J. S. Lee, L. Jurkevich, P. Dewaele, P. Wambacq, and A. Oosterlinck, Remote Sens. Rev. 8, 313 (1994).
[Crossref]

Katz, O.

O. Katz, P. Heidmann, M. Fink, and S. Gigan, Nat. Photonics 8, 784 (2014).
[Crossref]

Lee, J. S.

J. S. Lee, L. Jurkevich, P. Dewaele, P. Wambacq, and A. Oosterlinck, Remote Sens. Rev. 8, 313 (1994).
[Crossref]

Leendertz, J.

J. Leendertz, J. Phys. E 3, 214 (1970).
[Crossref]

Luk, F.

F. Luk, V. Huynh, and W. North, J. Phys. E 22, 977 (1989).
[Crossref]

McKenchnie, S.

C. Dainty, E. Ennos, M. Francon, J. Goodman, S. McKenchnie, and G. Parry, Laser Speckle and Related Phenomena (Springer, 1975).

McLachlan, G.

G. McLachlan, Discriminant Analysis and Statistical Pattern Recognition (Wiley, 2004).

North, W.

F. Luk, V. Huynh, and W. North, J. Phys. E 22, 977 (1989).
[Crossref]

Oosterlinck, A.

J. S. Lee, L. Jurkevich, P. Dewaele, P. Wambacq, and A. Oosterlinck, Remote Sens. Rev. 8, 313 (1994).
[Crossref]

Parry, G.

C. Dainty, E. Ennos, M. Francon, J. Goodman, S. McKenchnie, and G. Parry, Laser Speckle and Related Phenomena (Springer, 1975).

Ribbens, W. B.

Ridderb, D.

M. Egmont-Petersena, D. Ridderb, and H. Handelsc, Pattern Recognit. 35, 2279 (2002).
[Crossref]

Wambacq, P.

J. S. Lee, L. Jurkevich, P. Dewaele, P. Wambacq, and A. Oosterlinck, Remote Sens. Rev. 8, 313 (1994).
[Crossref]

Waterman-Storer, C.

G. Danuser and C. Waterman-Storer, Annu. Rev. Biophys. Biomol. Struct. 35, 361 (2006).
[Crossref]

Annu. Rev. Biophys. Biomol. Struct. (1)

G. Danuser and C. Waterman-Storer, Annu. Rev. Biophys. Biomol. Struct. 35, 361 (2006).
[Crossref]

Appl. Opt. (1)

J. Phys. E (2)

J. Leendertz, J. Phys. E 3, 214 (1970).
[Crossref]

F. Luk, V. Huynh, and W. North, J. Phys. E 22, 977 (1989).
[Crossref]

Nat. Photonics (1)

O. Katz, P. Heidmann, M. Fink, and S. Gigan, Nat. Photonics 8, 784 (2014).
[Crossref]

Pattern Recognit. (1)

M. Egmont-Petersena, D. Ridderb, and H. Handelsc, Pattern Recognit. 35, 2279 (2002).
[Crossref]

Remote Sens. Rev. (1)

J. S. Lee, L. Jurkevich, P. Dewaele, P. Wambacq, and A. Oosterlinck, Remote Sens. Rev. 8, 313 (1994).
[Crossref]

Other (7)

R. Erf, Speckle Metrology (Elsevier, 2012).

J. W. Goodman, Speckle Phenomena in Optics: Theory and Applications (Roberts & Company, 2007).

C. Dainty, E. Ennos, M. Francon, J. Goodman, S. McKenchnie, and G. Parry, Laser Speckle and Related Phenomena (Springer, 1975).

G. McLachlan, Discriminant Analysis and Statistical Pattern Recognition (Wiley, 2004).

CNN to predict materials from their corresponding speckles, https://doi.org/10.6084/m9.figshare.5777598 .

Parameter vectors from speckle images, https://doi.org/10.6084/m9.figshare.5777595 .

LDA to predict materials from their corresponding parameter vectors, https://doi.org/10.6084/m9.figshare.5777601 .

Supplementary Material (4)

NameDescription
» Code 1       CNN to predict materials from their corresponding speckles
» Code 2       Parameter vectors from speckle images
» Code 3       LDA to predict materials from their corresponding parameter vectors
» Supplement 1       Supplement 1

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Figures (4)

Fig. 1.
Fig. 1.

Experimental setup: He–Ne laser beam is pointed at the scattering sample. The sample emits speckles in all directions and the cameras detect a small subsection of this pattern. A polarizer (P) may be placed on the cameras to only view a desired polarization.

Fig. 2.
Fig. 2.

Predictive rates for several material sets. Data collected in single shot mode with: no polarizer, polarizer perpendicular to illumination, and parallel to illumination. (a) CNN algorithm. (b) LDA algorithm. White paints: two slabs of the same paint, dried with different surface roughness levels. White paper stacks: paper from two different manufacturers. Wood: Two pieces of different plywood. Powdered sugar and flour were encased in a petri dish. Four material set: slab of white paint, white plastic, wood, and white paper. Error bars are not presented since the errors are <1% in the LDA case (see Supplement 1) and ambiguous in the CNN case.

Fig. 3.
Fig. 3.

Cross sections of the normalized autocorrelation functions. ΓI(Δx,Δy;Δy=0) of speckles from four different materials averaged over 180 images. (a) Polarizer perpendicular to the illumination polarization; (b) no polarizer. Graphs of the difference between each material and the average of all four are presented in the upper left corners.

Fig. 4.
Fig. 4.

Differentiating between two white papers created by different manufacturers by inserting multiple images of their speckles into the LDA algorithm.

Equations (4)

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

I2=2I2.
C=1+η2/(1+η).
ΓI(Δx,Δy)=I¯2[1+|I(α,β)exp(i2πλz[αΔx+βΔy])dαdβI(α,β)dαdβ|2],
GI(ux,uy)=I¯2[δ(ux,uy)+I(α,β)I(α+λzux,β+λzuy)dαdβ[I(α,β)dαdβ]2].