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International Conference on Intelligent Science and Big Data Engineering

IScIDE 2015: Intelligence Science and Big Data Engineering. Image and Video Data Engineering pp 196–207Cite as

Image Denoising Using Modified Nonsubsampled Contourlet Transform Combined with Gaussian Scale Mixtures Model

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9242))

Abstract

Nonsubsampled contourlet transform (NSCT) combined with Gaussian scale mixtures model (GSM) has been recognized as an excellent method for image denoising. However, the processing performance of this method is highly relied on the performance of nonsubsampled directional filter bank (NSDFB) applied in NSCT. In this paper, we employ a lifting scheme to develop a new directional filter bank (DFB). The new DFB is adopted to improve the original NSDFB for a highly efficient NSCT. By combining with the GSM, the improved NSCT is particularly propitious for image denoising. The experimental results show that the modified NSCT significantly outperforms the traditional NSCT in processing performance while preserving good visual quality of denoised images.

C. Yan—This work is supported by the National Natural Science Foundation of China under Grant Nos. 61471161, 41461078, and 61367005, and by the Fundamental Research Funds for the Universities of Gansu Province.

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References

  1. Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3), 425–455 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  2. Donoho, D.L.: De-noising by soft-threshoding. Ann. Stat. 41(3), 613–627 (1995)

    MathSciNet  MATH  Google Scholar 

  3. Blu, T.: The SURE-LET approach to image denoising. IEEE Trans. Image Process. 16(11), 2778–2786 (2007)

    Article  MathSciNet  Google Scholar 

  4. Jiao, L.C., Tan, S.: Development and prospect of image multiscale geometric analysis. Acta Electronic Sinca 31(12), 1975–1981 (2003)

    Google Scholar 

  5. LeCun, Ponce, J.: Learning midlevel features for recognition. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 2559–2566 (2010)

    Google Scholar 

  6. Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res. 11, 19–60 (2006)

    MathSciNet  MATH  Google Scholar 

  7. Aharon, M., Elad, M., Bruckstein, A.M.: The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)

    Article  Google Scholar 

  8. Portilla, J., Strela, V., Wainwright, M., Simoncelli, E.P.: Image denoising using scale mixtures of gaussians in the wavelet domain. IEEE Trans. Image Process. 12(11), 1338–1351 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  9. Wainwright, M.J., Simoncelli, E.P.: Scale mixtures of Gaussians and the statistics of natural images. In: Advance in Neural Information Processing Systems, vol. 12, pp. 855–861 (2000)

    Google Scholar 

  10. Tasdizen, T.: Principal neighborhood dictionaries for nonlocal means image denoising. IEEE Trans. Image Process 18(12), 2649–2660 (2009)

    Article  MathSciNet  Google Scholar 

  11. Zhou, H.F., Wang, X.T., Xu, X.G.: Image denoising using gaussian scale mixture model in the nonsubsampled contourlet domain. J. Electron. Inf. Technol. 31(8), 1796–1800 (2009)

    Google Scholar 

  12. Yan, C.M., Guo, B.L., Yi, M.: Fast algorithm for nonsubsampled contourlet transform. Acta Automatica Sinica 44(4), 757–762 (2014)

    Article  Google Scholar 

  13. Do, M.N., Vetterli, M.: The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans. Image Process. 14(2), 2091–2106 (2005)

    Article  Google Scholar 

  14. Cunha, A.L., Zhou, J.P., Do, M.N.: The nonsubsampled Contourlet transform: theory, design and application. IEEE Trans. Image Process. 15(10), 3089–3101 (2006)

    Google Scholar 

  15. Bamberger, R.H., Smith, M.J.T.: A filter bank for the directional decomposition of images: Theory and design. IEEE Trans. Signal Process. 40(4), 882–893 (1992)

    Article  Google Scholar 

  16. Po, D.D.-Y., Do, M.N.: Directional multiscale modeling of images using the contourlet transform. IEEE Trans. Signal Process. 15(6), 1610–1620 (2006)

    MathSciNet  Google Scholar 

  17. Yi, C., Michael D., Adams, M. D., Lu, W.S.: Design of optimal quincunx filter banks for image coding. Journal on Advances in Signal Processing, Article ID 83858 (2007)

    Google Scholar 

  18. Sweldens, W.: The lifting scheme: a custom-design construction of biorthogonal. Appl. Comput. Harmon. Anal. 3(2), 186–200 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  19. Tran, T.D., de Queiroz, R.L., Nguyen, T.Q.: Linear-phase perfect reconstruction filter bank: Lattice structure, design, and application in image coding 48(1), 133–147 (2000)

    Google Scholar 

  20. Gouze, A., Antonini, M., Barlaud, M.: Quincunx lifting scheme for lossy image coding. In: IEEE International Conference on Image Processing, pp. 665–668 (2000)

    Google Scholar 

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Author information

Authors and Affiliations

  1. College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, 730070, Gansu, China

    Chunman Yan & Yunping Qi

  2. School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, China

    Kaibing Zhang

Authors
  1. Chunman Yan
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  2. Kaibing Zhang
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  3. Yunping Qi
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Corresponding author

Correspondence to Chunman Yan .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xiaofei He

  2. Xidian University, Xi'an, China

    Xinbo Gao

  3. Northwestern Polytechnical University, Shaanxi, China

    Yanning Zhang

  4. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  5. Chinese Academy of Sciences, Beijing, China

    Zhi-Yong Liu

  6. Suzhou University of Science and Technology, Suzhou, China

    Baochuan Fu

  7. Suzhou University of Science and Technology, Jiangsu, China

    Fuyuan Hu

  8. Suzhou University of Science and Technology, Jiangsu, China

    Zhancheng Zhang

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© 2015 Springer International Publishing Switzerland

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Yan, C., Zhang, K., Qi, Y. (2015). Image Denoising Using Modified Nonsubsampled Contourlet Transform Combined with Gaussian Scale Mixtures Model. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_21

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  • DOI: https://doi.org/10.1007/978-3-319-23989-7_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23987-3

  • Online ISBN: 978-3-319-23989-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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