OSA's Digital Library

Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 18, Iss. 2 — Jan. 18, 2010
  • pp: 1024–1032

Three-dimensional speckle suppression in optical coherence tomography based on the curvelet transform

Zhongping Jian, Lingfeng Yu, Bin Rao, Bruce J. Tromberg, and Zhongping Chen  »View Author Affiliations

Optics Express, Vol. 18, Issue 2, pp. 1024-1032 (2010)

View Full Text Article

Enhanced HTML    Acrobat PDF (617 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Optical coherence tomography is an emerging non-invasive technology that provides high resolution, cross-sectional tomographic images of internal structures of specimens. OCT images, however, are usually degraded by significant speckle noise. Here we introduce to our knowledge the first 3D approach to attenuating speckle noise in OCT images. Unlike 2D approaches which only consider information in individual images, 3D processing, by analyzing all images in a volume simultaneously, has the advantage of also taking the information between images into account. This, coupled with the curvelet transform’s nearly optimal sparse representation of curved edges that are common in OCT images, provides a simple yet powerful platform for speckle attenuation. We show the approach suppresses a significant amount of speckle noise, while in the mean time preserves and thus reveals many subtle features that could get attenuated in other approaches.

© 2010 OSA

OCIS Codes
(100.2980) Image processing : Image enhancement
(110.4500) Imaging systems : Optical coherence tomography
(110.6150) Imaging systems : Speckle imaging

ToC Category:
Imaging Systems

Original Manuscript: October 21, 2009
Revised Manuscript: December 14, 2009
Manuscript Accepted: December 18, 2009
Published: January 7, 2010

Virtual Issues
Vol. 5, Iss. 3 Virtual Journal for Biomedical Optics

Zhongping Jian, Lingfeng Yu, Bin Rao, Bruce J. Tromberg, and Zhongping Chen, "Three-dimensional speckle suppression in optical coherence tomography based on the curvelet transform," Opt. Express 18, 1024-1032 (2010)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical Coherence Tomography,” Science 254(5035), 1178–1181 (1991). [CrossRef] [PubMed]
  2. J. M. Schmitt, “Array detection for speckle reduction in optical coherence microscopy,” Phys. Med. Biol. 42(7), 1427–1439 (1997). [CrossRef] [PubMed]
  3. J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in Optical Coherence Tomography,” J. Biomed. Opt. 4(1), 95 (1999). [CrossRef]
  4. A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. Am. A 24(7), 1901 (2007). [CrossRef]
  5. D. L. Marks, T. S. Ralston, and S. A. Boppart, “Speckle reduction by I-divergence regularization in optical coherence tomography,” J. Opt. Soc. Am. A 22(11), 2366 (2005). [CrossRef]
  6. D. C. Adler, T. H. Ko, and J. G. Fujimoto, “Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter,” Opt. Lett. 29(24), 2878–2880 (2004). [CrossRef]
  7. M. Gargesha, M. W. Jenkins, A. M. Rollins, and D. L. Wilson, “Denoising and 4D visualization of OCT images,” Opt. Express 16(16), 12313–12333 (2008). [CrossRef] [PubMed]
  8. P. Puvanathasan and K. Bizheva, “Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set,” Opt. Express 15(24), 15747–15758 (2007). [CrossRef] [PubMed]
  9. S. H. Xiang, L. Zhou, and J. M. Schmitt, “Speckle Noise Reduction for Optical Coherence Tomography,” Proc. SPIE 3196, 79 (1997). [CrossRef]
  10. Z. Jian, Z. Yu, L. Yu, B. Rao, Z. Chen, and B. J. Tromberg, “Speckle Attenuation by Curvelet Shrinkage in Optical Coherence Tomography,” Opt. Lett. 34, 1516 (2009). [CrossRef] [PubMed]
  11. E. J. Candès, L. Demanet, D. L. Donoho, and L. Ying, “Fast Discrete Curvelet Transforms,” SIAM Multiscale Model. Simul. 5(3), 861 (2006). [CrossRef]
  12. E. J. Candès, and D. L. Donoho, “Curvelets–a surprisingly effective nonadaptive representation for objects with edges,” in Curves and Surface Fitting, C. Rabut, A. Cohen, and L. L. Schumaker, eds. (Vanderbilt University Press, Nashville, TN., 2000).
  13. E. J. Candès and D. L. Donoho, “New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities,” Commun. Pure Appl. Math. 57, 219 (2003). [CrossRef]
  14. J.-L. Starck, E. J. Candès, and D. L. Donoho, “The Curvelet Transform for Image Denoising,” IEEE Trans. Image Process. 11(6), 670–684 (2002). [CrossRef]
  15. B. Rao, L. Yu, H. K. Chiang, L. C. Zacharias, R. M. Kurtz, B. D. Kuppermann, and Z. Chen, “Imaging pulsatile retinal blood flow in human eye,” J. Biomed. Opt. 13(4), 040505 (2008). [CrossRef] [PubMed]
  16. S. G. Chang, B. Yu, and M. Vetterli, “Spatially adaptive wavelet thresholding with context modeling for image denoising,” IEEE Trans. Image Process. 9(9), 1522–1531 (2000). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited