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Bilateral filtering with adaptation to phase coherence and noise

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Abstract

In this paper, a bilateral filter with adaptive domain and range parameter is introduced for image denoising. Since the objective of denoising is to reduce noise as much as possible while preserving the perceptually important details, the parameters are adjusted in accordance with perceptual significance of pixels and noise level. The domain parameter is obtained by using the maximum and minimum moments of local phase coherence for being the representative of image details such as edges and corners of an image. The range parameter is estimated from the intensity-homogeneity measurements for their ability to represent the underlying noise. In addition, the filter is applied in an iterative manner to reduce the residual noise. Experiments are carried out using various standard images, and the results show that the proposed method is more effective in reducing additive white Gaussian noise as compared to several recently introduced denoising techniques in terms of the peak signal-to-noise ratio, structural similarity index and visual quality. In addition, experiments performed using real noisy images reveal the ability of the proposed filter to provide denoised images of better visual quality.

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References

  1. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: IEEE International Conference on Computer Vision, pp. 839–846 (1998)

  2. Wong A.: Adaptive bilateral filtering of image signals using local phase characteristics. Signal Process. 88(6), 1615–1619 (2008)

    Article  MATH  Google Scholar 

  3. Xie J., Heng P.A., Shah M.: Image diffusion using saliency bilateral filter. IEEE Trans. Info. Tech. Biomed. 12(6), 768–771 (2008)

    Article  Google Scholar 

  4. Xie, J., Heng, P.A.: Color image diffusion using adaptive bilateral filter. In: Proceedings of IEEE Engineering in Medicine and Biology, pp. 3433–3436, September, 2005

  5. Peng, H., Rao, R.: Bilateral Kernel parameter optimization by risk minimization. In: IEEE International Conference on Image Processing, pp. 3293–3296, September, 2010

  6. Kisilev P., Freedman D.: Parameter tuning by pairwise preferences. IEEE Trans. Info. Tech. Biomed. 12(6), 1–11 (2010)

    Google Scholar 

  7. Kovesi, P.: Phase congruency detects corners and edges. In: The Australian Pattern Recognition Society Conference, pp. 309–318 (2003)

  8. Zhang M., Gunturk B.K.: Multiresolution bilateral filtering for image denoising. IEEE Trans. Image Process. 17(12), 2324–2332 (2008)

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  10. Butt, I.T., Rajpoot, N.M.: Multilateral filtering: a novel framework for generic similarity-based image denoising. In: IEEE International Conference on Image Processing, November, 2009

  11. Lu, Y., Do, M.N.: A new contourlet transform with sharp frequency localization. In: IEEE International Conference on Image Processing, pp. 1629–1632 (2006)

  12. Wang, Z., Simoncelli, E.: Local phase coherence and the perception of blur. In: Advances in Neural Information Processing Systems, vol. 16, May 2004

  13. Amer A., Dubois E.: Fast and reliable structure-oriented video noise estimation. IEEE Trans. Circuits Syst. Video Tech. 15(1), 113–118 (2005)

    Article  Google Scholar 

  14. http://www.csse.uwa.edu.au/pk/Research/MatlabFns/

  15. Kovesi P.: Image features from phase congruency. Videre: J. Comput. Vis. Res. 1(3), 1–26 (1999)

    Google Scholar 

  16. Elad M.: On the origin of the bilateral filter and the ways to improve it. IEEE Trans. Image Process. 11(10), 1141–1151 (2002)

    Article  MathSciNet  Google Scholar 

  17. Barash D.: Fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation . IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 844–847 (2002)

    Article  Google Scholar 

  18. 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)

    Article  Google Scholar 

  19. Luisier F., Blu T., Unser M.: A new sure approach to image denoising: inter-scale orthonormal wavelet thresholding. IEEE Trans. Image Process. 16(3), 593–606 (2007)

    Article  MathSciNet  Google Scholar 

  20. Sha’ashua, A., Ullman, S.: Structural saliency: the detection of globally salient structures using a locally connected network. In: IEEE International Conference on Computer Vision, pp. 321–327 (1988)

  21. http://bigwww.epfl.ch/demo/suredenoising/

  22. Color Test Images. http://decsai.ugr.es/~javier/denoise April 2008

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Correspondence to M. I. H. Bhuiyan.

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Farzana, E., Tanzid, M., Mohsin, K.M. et al. Bilateral filtering with adaptation to phase coherence and noise. SIViP 7, 367–376 (2013). https://doi.org/10.1007/s11760-011-0253-5

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  • DOI: https://doi.org/10.1007/s11760-011-0253-5

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