Skip to main content
SpringerLink
Log in

Local thresholding with adaptive window shrinkage in the contourlet domain for image denoising

  • Research Paper
  • Special Focus
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Threshold selection is a challenging job for the image denoising in the contourlet domain. In this paper, a new local threshold with adaptive window shrinkage is proposed. According to the anisotropic energy clusters in contourlet subbands, local adaptive elliptic windows are introduced to determine the neighboring coefficients with strong dependencies for each coefficient. Utilizing the maximum likelihood estimator within the adaptive window, the signal variance is estimated from the noisy neighboring coefficients. Based on the signal variance estimation, the new threshold is obtained in the Bayesian framework. Since it makes full use of the captured directional information of images, the threshold extends to the anisotropic spatial adaptability and behaves reliably. Simulation experiments show that the new method exhibits better performance than other outstanding wavelet and contourlet denoising schemes obviously, both in the PSNR value and the visual appearance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Fu S J, Zhang C M, Tai X C. Image denoising and deblurring: non-convex regularization, inverse diffusion and shock filter. Sci China Inf Sci, 2011, 54: 1184–1198

    Article  MathSciNet  MATH  Google Scholar 

  2. Romberg J K, Wakin M B, Baraniuk R G. Multiscale geometric image processing. In: Proceedings of VCIP, Lugano, 2003. 1265–1272

  3. Rahman S M M, Hasan M K. Wavelet-domain iterative center weighted median filter for image denoising. Signal Process, 2003, 83: 1001–1012

    Article  MATH  Google Scholar 

  4. Chen GY, Bui TD, Krzyżak A. Image denoising with neighbour dependency and customized wavelet and threshold. Patt Recog, 2005, 38: 115–124

    Article  Google Scholar 

  5. Zhao R Z, Liu X Y, Li C C. Wavelet denoising via sparse representation. Sci China Ser F-Inf Sci, 2009, 52: 1371–1377

    Article  MathSciNet  MATH  Google Scholar 

  6. Chang S G, Yu B, Vetterli M. Adaptive wavelet thresholding for image denoising and compression. IEEE Trans Image Process, 2000, 9: 1532–1546

    Article  MathSciNet  MATH  Google Scholar 

  7. Nasri M, Nezamabadi-pour H. Image denoising in the wavelet domain using a new adaptive thresholding function. Neurocomputing, 2009, 72: 1012–1025

    Article  Google Scholar 

  8. Do M N, Vetterli M. The finite ridgelet transform for image representation. IEEE Trans Image Process 2003, 12, 16–28

    Article  MathSciNet  Google Scholar 

  9. Starck J L, Candes E J, Donoho D L. The curvelet transform for image denoising. IEEE Trans Image Process, 2002, 11: 670–684

    Article  MathSciNet  Google Scholar 

  10. Do M N, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process, 2005, 14: 2091–2106

    Article  MathSciNet  Google Scholar 

  11. Shan H, Ma J W, Yang H Z. Comparisons of wavelets, contourlets and curvelets in seismic denoising. J Appl Geophys, 2009, 69: 103–115

    Article  Google Scholar 

  12. Wang X H, Chen M Y, Song C M, et al. Contourlet HMT model with directional feature. Sci China Inf Sci, 2012, 55: 1563–1578

    Article  MathSciNet  MATH  Google Scholar 

  13. Shen X H, Zhang C M. Texture image segmentation based on contourlet-domain contextual hidden Markov tree model. ICIC Express Lett, 2011, 5: 3707–3712

    MathSciNet  Google Scholar 

  14. Zhang X, Jing X L. Image denoising in contourlet domain based on a normal inverse Gaussian prior. Digit Signal Process, 2010, 20: 1439–1446

    Article  Google Scholar 

  15. Da Cunha A L, Zhou J P, Do M N, The nonsubsampled contourlet transform: theory, design and application. IEEE Trans Image Process, 2006, 15: 3089–3101

    Article  Google Scholar 

  16. Eom I K, Kim Y S. Wavelet-based denoising with nearly arbitrarily shaped windows. IEEE Signal Process Lett, 2004, 11: 937–940

    Article  Google Scholar 

  17. Shui P L. Image denoising algorithm via doubly local wiener filtering with directional windows in wavelet domain. IEEE Signal Process Lett, 2005, 12: 681–684

    Article  Google Scholar 

  18. Chang S G, Yu B, Vetteri M. Spatially adaptive wavelet thresholding with context modeling for image denosing. IEEE Trans Image Process, 2000, 9: 1522–1531

    Article  MathSciNet  MATH  Google Scholar 

  19. Boykov Y, Veksler P, Zabih R. A variable window approach to early vision. IEEE Trans Patt Anal Mach Intell, 1998, 20: 1283–1294

    Article  Google Scholar 

  20. Long Z L, Younan N H. Statistical image modeling in the contourlet domain using contextual hidden Markov models. Signal Process, 2009, 89: 946–951

    Article  MATH  Google Scholar 

  21. Po D D Y, Do M N. Directional multiscale modeling of images using the contourlet transform. IEEE Trans Image Process, 2006, 15: 1610–1620

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, China

    XiaoHong Shen, Kai Wang & Qiang Guo

  2. Shandong Provincial Key Laboratory of Digital Media Technology, Shandong University of Finance and Economics, Jinan, 250014, China

    XiaoHong Shen, Kai Wang & Qiang Guo

Authors
  1. XiaoHong Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qiang Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to XiaoHong Shen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shen, X., Wang, K. & Guo, Q. Local thresholding with adaptive window shrinkage in the contourlet domain for image denoising. Sci. China Inf. Sci. 56, 1–9 (2013). https://doi.org/10.1007/s11432-013-4988-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-013-4988-1

Keywords

Search

Navigation