OSA's Digital Library

Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 3, Iss. 12 — Dec. 1, 2012
  • pp: 3231–3239

Dual tree complex wavelet transform based denoising of optical microscopy images

Ufuk Bal  »View Author Affiliations

Biomedical Optics Express, Vol. 3, Issue 12, pp. 3231-3239 (2012)

View Full Text Article

Enhanced HTML    Acrobat PDF (2000 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Photon shot noise is the main noise source of optical microscopy images and can be modeled by a Poisson process. Several discrete wavelet transform based methods have been proposed in the literature for denoising images corrupted by Poisson noise. However, the discrete wavelet transform (DWT) has disadvantages such as shift variance, aliasing, and lack of directional selectivity. To overcome these problems, a dual tree complex wavelet transform is used in our proposed denoising algorithm. Our denoising algorithm is based on the assumption that for the Poisson noise case threshold values for wavelet coefficients can be estimated from the approximation coefficients. Our proposed method was compared with one of the state of the art denoising algorithms. Better results were obtained by using the proposed algorithm in terms of image quality metrics. Furthermore, the contrast enhancement effect of the proposed method on collagen fıber images is examined. Our method allows fast and efficient enhancement of images obtained under low light intensity conditions.

© 2012 OSA

OCIS Codes
(100.0100) Image processing : Image processing
(100.3020) Image processing : Image reconstruction-restoration
(100.7410) Image processing : Wavelets

ToC Category:
Image Processing

Original Manuscript: August 1, 2012
Revised Manuscript: November 7, 2012
Manuscript Accepted: November 9, 2012
Published: November 13, 2012

Ufuk Bal, "Dual tree complex wavelet transform based denoising of optical microscopy images," Biomed. Opt. Express 3, 3231-3239 (2012)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. S. Delpretti, F. Luisier, S. Ramani, T. Blu, and M. Unser, “Multiframe sure-let denoising of timelapse fluorescence microscopy images,” in 5th IEEE International Symposium on Biomedical Imaging: from Nano to Macro, 2008. ISBI 2008 (IEEE, 2008), pp. 149–152.
  2. C. Vonesch, F. Aguet, J. L. Vonesch, and M. Unser, “The colored revolution of bioimaging,” IEEE Signal Process. Mag.23(3), 20–31 (2006). [CrossRef]
  3. Q. Wu, F. A. Merchant, and K. R. Castleman, Microscope Image Processing (Academic, Amsterdam, 2008).
  4. F. Luisier, T. Blu, and M. Unser, “Image denoising in mixed Poisson-Gaussian noise,” IEEE Trans. Image Process.20(3), 696–708 (2011). [CrossRef] [PubMed]
  5. F. J. Anscombe, “The transformation of Poisson, binomial and negative-binomial data,” Biometrika35, 246–254 (1948).
  6. D. L. Donoho, “Nonlinear wavelet methods for recovery of signals, densities, and spectra from indirect and noisy data,” in Proceedings of Symposia in Applied Mathematics. Vol 47. Different Perspectives on Wavelets, I. Daubechies, ed. (American Mathematical Society, 1993).
  7. D. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inf. Theory41(3), 613–627 (1995). [CrossRef]
  8. P. Fryzlewicz and G. P. Nason, “A Haar-Fisz algorithm for Poisson intensity estimation,” J. Comput. Graph. Statist.13(3), 621–638 (2004). [CrossRef]
  9. F. Luisier, C. Vonesch, T. Blu, and M. Unser, “Fast interscale wavelet denoising of Poisson-corrupted images,” Signal Process.90(2), 415–427 (2010). [CrossRef]
  10. I. W. Selesnick, R. G. Baraniuk, and N. C. Kingsbury, “The dual-tree complex wavelet transform,” IEEE Signal Process. Mag.22(6), 123–151 (2005). [CrossRef]
  11. N. Kingsbury, “The dual-tree complex wavelet transform: a new efficient tool for image restoration and enhancement,” in Proceedings of the 9th European Signal Processing Conference (EUSIPCO 98), (Typorama, 1998), pp. 319–322.
  12. F. Daniels, “Quantification of collagen orientation in 3D engineered tissue,” in Biomedical Engineering (Eindhoven University of Technology, Eindhoven, 2006).
  13. N. Kingsbury, “The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters,” in Proceedings of the 8th IEEE DSP Workshop, Utah (IEEE, 1998), Vol. 8, p. 86
  14. V. Musoko, “Biomedical signal and image processing,” in Computing and Control Engineering (Institute of Chemical Technology, Prague, 2005).
  15. A. Salih Husain and S. Aymen Dawood, “Image compression based on 2D dual tree complex wavelet transform,” Eng. Technol. J.28, 1290–1305 (2010).
  16. H. M. Salinas and D. C. Fernández, “Comparison of PDE-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography,” IEEE Trans. Med. Imaging26(6), 761–771 (2007). [CrossRef] [PubMed]

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.


Fig. 1 Fig. 2 Fig. 3
Fig. 4

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited