Signal, Image and Video Processing

, Volume 13, Issue 7, pp 1347–1355 | Cite as

Power shrinkage—curvelet domain image denoising using a new scale-dependent shrinkage function

  • Oussama KadriEmail author
  • Zine-Eddine Baarir
  • Gerald Schaefer
Original Paper


Image processing and analysis algorithms are at the heart of applications in various scientific fields, such as medical diagnosis, military imaging, and astronomy. However, images are typically exposed to noise contamination during their acquisition and transmission. In this paper, we explore recent advancements in image denoising using curvelet domain shrinkage and present a novel scale-dependent shrinkage function, which we call power shrinkage, to enhance restored image quality. Experimental results confirm our proposed method to perform better than classical thresholding and to outperform recent state-of-the-art approaches in denoising different types of noises including speckle, Poisson and additive white Gaussian noise.


Image noise Image quality Image denoising Curvelet shrinkage Power shrinkage 



  1. 1.
    Ali, A.D., Swami, P.D., Singhai, J.: Modified curvelet thresholding algorithm for image denoising. J. Comput. Sci. 6, 18–23 (2010)CrossRefGoogle Scholar
  2. 2.
    Anisimova, E., Bednar, J., Pata, P.: Astronomical image denoising using curvelet and starlet transform. In: 23rd international conference radioelektronika, pp. 255–260 (2013)Google Scholar
  3. 3.
    Bruce, A.G., Gao, H.Y.: WaveShrink: shrinkage functions and thresholds. Proc. SPIE 2569, 270–281 (1995)CrossRefGoogle Scholar
  4. 4.
    Candès, E., Demanet, L., Donoho, D., Ying, L.: Fast discrete curvelet transforms. Multiscale Model. Simul. 5(3), 861–899 (2006)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Candès, E., Demanet, L., Lexing, Y.: Curvelab toolbox ver 2.0.3. California Institute of Technology, Pasadena (2007)Google Scholar
  6. 6.
    Candès, E., Donoho, D.: Curvelets: A surprisingly effective nonadaptive representation of objects with edges. In: Curve and Surface Fitting, pp. 105–120 (1999)Google Scholar
  7. 7.
    Candès, E.J., Donoho, D.L.: New tight frames of curvelets and optimal representations of objects with piecewise \(c^2\) singularities. Commun. Pure Appl. Math. 57(2), 219–266 (2003)CrossRefGoogle Scholar
  8. 8.
    Chen, Z., Wang, S., Fang, G., Wang, J.: Ionograms denoising via curvelet transform. Adv. Space Res. 52(7), 1289–1296 (2013)CrossRefGoogle Scholar
  9. 9.
    Cui, H., Yan, G., Song, H.: A novel curvelet thresholding denoising method based on chi-squared distribution. Signal Image Video Process. 9(2), 491–498 (2015)CrossRefGoogle Scholar
  10. 10.
    Donoho, D.L.: De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41(3), 613–627 (1995)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Gao, H.Y.: Wavelet shrinkage denoising using the non-negative Garrote. J. Comput. Graph. Stat. 7(4), 469–488 (1998)MathSciNetGoogle Scholar
  12. 12.
    Gorszczyk, A., Adamczyk, A., Malinowski, M.: Application of curvelet denoising to 2D and 3D seismic data—practical considerations. J. Appl. Geophys. 105, 78–94 (2014)CrossRefGoogle Scholar
  13. 13.
    Kamble, V.M., Parlewar, P., Keskar, A.G., Bhurchandi, K.M.: Performance evaluation of wavelet, ridgelet, curvelet and contourlet transforms based techniques for digital image denoising. Artif. Intell. Rev. 45(4), 509–533 (2016)CrossRefGoogle Scholar
  14. 14.
    Norouzzadeh, Y., Jampour, M.: A novel curvelet thresholding function for additive Gaussian noise removal. Int. J. Comput. Theory Eng. pp. 543–546 (2011)Google Scholar
  15. 15.
    Patil, A.A., Singhai, J.: Image denoising using curvelets transform—an approach to edge preserving. J. Sci. Indus. Res. 69(1), 34–38 (2010)Google Scholar
  16. 16.
    Raju, C., Reddy, T.S., Sivasubramanyam, M.: Denoising of remotely sensed images via curvelet transform and its relative assessment. In: 12th international multi-conference on information processing, pp. 771–777 (2016)CrossRefGoogle Scholar
  17. 17.
    Starck, J.L., Candès, E., Donoho, D.L.: The curvelet transform for image denoising. IEEE Trans. Image Process. 11(6), 670–684 (2002)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Sugantharathnam, D.M., Manimegalai, D.: The curvelet approach for denoising in various imaging modalities using different shrinkage rules. Int. J. Comput. Appl. 29(7), 36–42 (2011)Google Scholar
  19. 19.
    Swamy, P.S., Vani, K.: A novel thresholding technique in the curvelet domain for improved speckle removal in SAR images. Optik Int. J. Light Electron Opt. 127(2), 634–637 (2016)CrossRefGoogle Scholar
  20. 20.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.LESIA Laboratory of research, Electrical Engineering DepartmentMohammed Khider UniversityBiskraAlgeria
  2. 2.Department of Computer ScienceLoughborough UniversityLoughboroughUK

Personalised recommendations