Abstract

Holographic imaging may become severely degraded by a mixture of speckle and incoherent additive noise. Bayesian approaches reduce the incoherent noise, but prior information is needed on the noise statistics. With no prior knowledge, one-shot reduction of noise is a highly desirable goal, as the recording process is simplified and made faster. Indeed, neither multiple acquisitions nor a complex setup are needed. So far, this result has been achieved at the cost of a deterministic resolution loss. Here we propose a fast non-Bayesian denoising method that avoids this trade-off by means of a numerical synthesis of a moving diffuser. In this way, only one single hologram is required as multiple uncorrelated reconstructions are provided by random complementary resampling masks. Experiments show a significant incoherent noise reduction, close to the theoretical improvement bound, resulting in image-contrast improvement. At the same time, we preserve the resolution of the unprocessed image.

© 2013 Optical Society of America

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. S. Sotthivirat and J. A. Fessler, J. Opt. Soc. Am. A 21, 737 (2004).
    [CrossRef]
  2. A. Uzan, Y. Rivenson, and A. Stern, Appl. Opt. 52, A195 (2013).
    [CrossRef]
  3. J. G. Sucerquia, J. A. H. Ramirez, and D. V. Prieto, Optik 116, 44 (2005).
    [CrossRef]
  4. N. Bertaux, Y. Frauel, P. Réfrégier, and B. Javidi, J. Opt. Soc. Am. A 21, 2283 (2004).
    [CrossRef]
  5. P. Memmolo, I. Esnaola, A. Finizio, M. Paturzo, P. Ferraro, and A. M. Tulino, Opt. Express 20, 17250 (2012).
    [CrossRef]
  6. B. Javidi, P. Ferraro, S. Hong, and D. Alfieri, Opt. Lett. 30, 144 (2005).
    [CrossRef]
  7. L. Rong, W. Xiao, F. Pan, S. Liu, and R. Li, Chin. Opt. Lett. 8, 653 (2010).
    [CrossRef]
  8. S. Kubota and J. W. Goodman, Appl. Opt. 49, 4385 (2010).
    [CrossRef]
  9. J. I. Trisnadi, Proc. SPIE 4657, 131 (2002).
    [CrossRef]
  10. Y. Kuratomi, K. Sekiya, H. Satoh, T. Tomiyama, T. Kawakami, B. Katagiri, Y. Suzuki, and T. Uchida, J. Opt. Soc. Am. A 27, 1812 (2010).
    [CrossRef]
  11. F. T. S. Yu and E. Y. Wang, Appl. Opt. 12, 1656 (1973).
    [CrossRef]
  12. J. Maycock, B. M. Hennelly, J. B. Mc Donald, Y. Frauel, A. Castro, B. Javidi, and T. J. Naughton, J. Opt. Soc. Am. A 24, 1617 (2007).
    [CrossRef]
  13. M. Abollasshani and Y. Rostami, Optik 123, 937 (2012).
    [CrossRef]
  14. Y. S. Choi and S. J. Lee, Appl. Opt. 48, 2983 (2009).
    [CrossRef]

2013 (1)

2012 (2)

2010 (3)

2009 (1)

2007 (1)

2005 (2)

B. Javidi, P. Ferraro, S. Hong, and D. Alfieri, Opt. Lett. 30, 144 (2005).
[CrossRef]

J. G. Sucerquia, J. A. H. Ramirez, and D. V. Prieto, Optik 116, 44 (2005).
[CrossRef]

2004 (2)

2002 (1)

J. I. Trisnadi, Proc. SPIE 4657, 131 (2002).
[CrossRef]

1973 (1)

Abollasshani, M.

M. Abollasshani and Y. Rostami, Optik 123, 937 (2012).
[CrossRef]

Alfieri, D.

Bertaux, N.

Castro, A.

Choi, Y. S.

Esnaola, I.

Ferraro, P.

Fessler, J. A.

Finizio, A.

Frauel, Y.

Goodman, J. W.

Hennelly, B. M.

Hong, S.

Javidi, B.

Katagiri, B.

Kawakami, T.

Kubota, S.

Kuratomi, Y.

Lee, S. J.

Li, R.

Liu, S.

Maycock, J.

Mc Donald, J. B.

Memmolo, P.

Naughton, T. J.

Pan, F.

Paturzo, M.

Prieto, D. V.

J. G. Sucerquia, J. A. H. Ramirez, and D. V. Prieto, Optik 116, 44 (2005).
[CrossRef]

Ramirez, J. A. H.

J. G. Sucerquia, J. A. H. Ramirez, and D. V. Prieto, Optik 116, 44 (2005).
[CrossRef]

Réfrégier, P.

Rivenson, Y.

Rong, L.

Rostami, Y.

M. Abollasshani and Y. Rostami, Optik 123, 937 (2012).
[CrossRef]

Satoh, H.

Sekiya, K.

Sotthivirat, S.

Stern, A.

Sucerquia, J. G.

J. G. Sucerquia, J. A. H. Ramirez, and D. V. Prieto, Optik 116, 44 (2005).
[CrossRef]

Suzuki, Y.

Tomiyama, T.

Trisnadi, J. I.

J. I. Trisnadi, Proc. SPIE 4657, 131 (2002).
[CrossRef]

Tulino, A. M.

Uchida, T.

Uzan, A.

Wang, E. Y.

Xiao, W.

Yu, F. T. S.

Supplementary Material (2)

» Media 1: MOV (1086 KB)     
» Media 2: AVI (3220 KB)     

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 (6)

Fig. 1.
Fig. 1.

(Media 1) Block scheme of the proposed denoising method.

Fig. 2.
Fig. 2.

Reconstructed amplitude images of a toy car. (a) SL image. (b) ML image processed with FCM. (c) (Media 2) ML image processed with ROMs. (d) Details of the edges corresponding to the red boxes in (a) for various reconstructions.

Fig. 3.
Fig. 3.

ML improvement in noise reduction, measured by the dispersion index of Eq. (4). (a) FCM: improvement [%] versus N. Different curves are obtained by varying the ratio A. (b) ROM: improvement [%] versus A [%].

Fig. 4.
Fig. 4.

Dispersion index versus N. A Comparison between FCM and ROM is shown. Dotted curve: theoretical improvement bound.

Fig. 5.
Fig. 5.

Relative deviation [Eq. (5)] in a homogeneous area of reconstructed image. (a) SL image. (b) ML image. (c) Normalized Laplacian versus A. Blue (triangles), ML-FCM. Red (circles), ML-ROM.

Fig. 6.
Fig. 6.

Normalized image amplitudes plotted at a fixed row along the columns of the test area indicated by the red dashed box in Fig. 2(b). The SL and ML images show the same trends.

Equations (6)

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

ISL(x,y)=|Fr[H(u,v)]|,
IML(x,y)=1Nn=1N|Fr[H(u,v)Mn(u,v)]|,
Mn(u,v)={0if(u,v)Sn1otherwise
D=σMLμMLμSLσSL,
R(x,y)=I2(x,y)μI2μI2,
n=1NMn=(N1)O,

Metrics