A New Similarity Measure for Non-local Means Denoising (original) (raw)

Abstract

Non-local means (NLM) denoising algorithm is a good similarity measure based denoising algorithm for images with repetitive textures. However, NLM cannot handle the large rotation. In this paper, we propose a rotation-invariant and noise-resistant similarity measure based on improved LBP operator, and use it to search for similar image patches. In addition, in order to speed up the algorithm, an automatic selection strategy of similar patches is proposed. Consequently, the self-similarity can be used to obtain more similar patches for denoising. Experiment results demonstrate that the proposed method achieved higher peak signal-to-noise ratio (PSNR) and more visual pleasing results than some state-of-art methods.

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References

  1. Huang, H., Lee, T.: Data adaptive median filters for signal and image denoising using a generalized SURE criterion. IEEE Signal Processing Letters 13(9), 561–564 (2006)
    Article Google Scholar
  2. Yuan, S., Tan, Y.: Impulse noise removal by a global noise detector and adaptive median filter. Signal Processing. 86(8), 2123–2128 (2006)
    Article MATH Google Scholar
  3. Buades, A., Coll, B., Morel, J.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 60-65. San Diego (2005)
    Google Scholar
  4. Sender, L., Selesnick, I.: Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Transactions on Signal Processing 20(11), 2744–2756 (2002)
    Article Google Scholar
  5. Portilla, J., Strela, V., Wainwright, M.: Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Transactions on Image Processing 12(11), 1338–1351 (2003)
    Article MathSciNet MATH Google Scholar
  6. Yin, M., Liu, W., Zhao, X.: Image denoising using trivariate prior model in nonsubsampled dual-tree complex contourlet transform domain and non-local means filter in spatial domain. Optik - International Journal for Light and Electron Optics 124(24), 6896–6904 (2013)
    Article Google Scholar
  7. Michael, E.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing 15(12), 3736–3745 (2006)
    Article MathSciNet Google Scholar
  8. Chen, G., Xiong, C., Corso, J.: Dictionary transfer for image denoising via domain adaptation. In: Proceedings of IEEE International Conference on Image Processing, pp. 1189-1192 (2012)
    Google Scholar
  9. Sun, D., Gao, Q., Lu, Y.: A novel image denoising algorithm using linear Bayesian MAP estimation based on sparse representation. Signal Processing 100, 132–145 (2014)
    Article Google Scholar
  10. Tasdizen, T.: Principal neighborhood dictionaries for nonlocal means image denoising. IEEE Transactions on Image Processing 18(12), 2649–2660 (2009)
    Article MathSciNet Google Scholar
  11. Grewenig, S., Zimmer, S., Weickert, J.: Rotationally invariant similarity measures for nonlocal image denoising. Journal of Visual Communication and Image Representation 22(2), 117–130 (2011)
    Article Google Scholar
  12. Deledalle, C., Denis, L., Tupin, F.: Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Transactions on Image Processing 18(12), 2661–2672 (2009)
    Article MathSciNet Google Scholar
  13. Dabov, K., Foi, A., Katkovnik, V.: Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Transactions on Image Processing 16(8), 2080–2095 (2007)
    Article MathSciNet Google Scholar
  14. Yue, W., Brian, T., Premkumar, N., Joseph, P.: Probabilistic Non-Local Means. IEEE Signal Processing Letters 20(8), 763–766 (2013)
    Article Google Scholar
  15. Deledalle, C., Duval, V., Salmon, J.: Non-Local Methods with Shape-Adaptive Patches (NLM-SAP). Journal of Mathematical Imaging and Vision 43(2), 103–120 (2012)
    Article MathSciNet MATH Google Scholar
  16. Mahmoudi, M., Sapiro, G.: Fast image and video denoising via nonlocal means of similar neighborhoods. IEEE Signal Processing Letters 12(12), 839–842 (2005)
    Article Google Scholar
  17. Coupé, P., Yger, P., Prima, S.: An optimized blockwise nonlocal means denoising filter for 3-D magnetic images. IEEE Transactions on Medical Imaging 27(4), 425–441 (2008)
    Article Google Scholar
  18. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
    Article MATH Google Scholar
  19. Zhou, W., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing. 13(4), 600–612 (2004)
    Article Google Scholar
  20. Alessandroa, F., Vladimir, K., Karen, E.: Pointwise shape adaptive DCT for high quality denoising and deblocking of grayscale and color images. IEEE Transactions on Image Processing. 16(5), 1395–1411 (2007)
    Article MathSciNet Google Scholar
  21. Knaus, C., Zwicker, M.: Dual-domain image denoising. In: IEEE International Conference on Image Processing, pp. 440-444 (2013)
    Google Scholar
  22. Chaudhury, K.N.: Acceleration of the shiftable O(1) algorithm for bilateral filtering and non-local means. IEEE Transactions on Image Proc. 22(4), 1291–1300 (2013)
    Article Google Scholar

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Authors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, Anhui, China
    Bin Cai, Wei Liu, Zhong Zheng & Zengfu Wang
  2. School of Information Science and Technology, University of Science and Technology of China, Hefei, 230027, Anhui, China
    Bin Cai & Zengfu Wang

Authors

  1. Bin Cai
  2. Wei Liu
  3. Zhong Zheng
  4. Zengfu Wang

Corresponding author

Correspondence toZengfu Wang .

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Editors and Affiliations

  1. Peking University, Beijing, China
    Honbin Zha
  2. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China
    Xilin Chen
  3. Chinese Academy of Sciences, Beijing, China
    Liang Wang
  4. Xidian University, Shaanxi, China
    Qiguang Miao

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© 2015 Springer-Verlag Berlin Heidelberg

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Cai, B., Liu, W., Zheng, Z., Wang, Z. (2015). A New Similarity Measure for Non-local Means Denoising. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds) Computer Vision. CCCV 2015. Communications in Computer and Information Science, vol 546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48558-3\_31

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