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Edge preservation of impulse noise filtered images by improved anisotropic diffusion

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Abstract

This paper provides a robust scheme for random valued impulsive noise reduction along with edge preservation by anisotropic diffusion with improved diffusivity. The defective impulse noisy pixels are detected by Laplacian based second order pixel difference operation where these defective pixels are replaced by appropriate values with regard of the gray level of their four directional neighbors. This de-noised image undergoes the diffusion operation where diffusion coefficient function is modified to make it adaptive by incorporating local gray level variance information. The proposed modified diffusion scheme effectively restore the edges and fine details destroyed during impulse noise reduction process. The effect of proposed diffusion scheme has been studied on various images and the results are compared with some existing diffusion methods which are independently used for impulse noise reduction and edge preservation. The results shows that the prior removal of impulsive noise before the application of diffusion process is advantageous over the direct application of diffusion for removing the impulsive noise. In addition, the results of the proposed diffusion scheme are compared with some of the median filter based methods which are effectively used for impulse noise reduction without caring of edge preservation. The proposed diffusion scheme sufficiently preserves the edges without boosting of impulsive noise components on images corrupted up to 50 % of the impulsive noise density.

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References

  1. Brownrigg D (1984) The weighted median filter. Commun ACM 27(8):807–818

    Article  Google Scholar 

  2. Cai JF, Chan RH, Nikolova M (2010) Fast two-phase image deblurring under impulse noise. J Math Imaging 36:46–53

    Article  MathSciNet  Google Scholar 

  3. Catte F, Lions PL, Morel JM, Coll T (1992) Image selective smoothing and edge-detection by nonlinear diffusion. SIAM J Numer Anal 29:182–193

    Article  MathSciNet  MATH  Google Scholar 

  4. Chao SM, Tsai DM (2010) An improved anisotropic diffusion model for detail and edge preserving smoothing. Pattern Recogn Lett 31:2012–2023

    Article  Google Scholar 

  5. Chen T, Wu HR (2001) Adaptive impulse detection using center weighted median filters. IEEE Signal Process Lett 8(1):1–3

    Article  Google Scholar 

  6. Chen Y, Barcelos C, Mair B (2001) Smoothing and edge detection by time-varying coupled nonlinear diffusion equations. Comp Vision Image Underst 82:85–100

    Article  MATH  Google Scholar 

  7. Chen Q, Wu D (2010) Image denoising by bounded block matching and 3D filtering. Signal Process 90:2778–2783

    Article  MATH  Google Scholar 

  8. Chao LT (2007) A new adaptive center weighted median filter for suppressing impusive noise in images. Inf Sci 177:1073–1087

    Article  Google Scholar 

  9. Crnojevic V, Senk V, Tropovski Z (2004) Advanced impulse detection based on pixel-wise MAD. IEEE Signal Process Lett 11(7):589–592

    Article  Google Scholar 

  10. Delon J, Desolneux A (2012) A patch-based approach for random-valued impulse noise removal. In: Proceedings of 2012 IEEE international conference on acoustic, speech and signal processing, (ICASSP 2012). Kyoto, Japan, 25–30 March 2012, pp 1093–1096

  11. Dong Y, Xu S (2007) A new directional weighted median filter for removal of random valued impulsive noise. IEEE Signal Process Lett 14(3):193–196

    Article  Google Scholar 

  12. Fang D, Nanning Z, Jianru X (2008) Image smoothing and sharpening based on nonlinear diffusion equation. Signal Process 88:2850–2855

    Article  MATH  Google Scholar 

  13. Gonzalez RC, Woods RE (2008) Digital image processing, ch 3, pp 144–168. Prentice Hall

  14. Ji Z, Chen Q, Sun QS, Xia DS (2009) A moment-based nonlocal-means algorithm for image denoising. Inf Process Lett 109:1238–1244

    Article  MathSciNet  MATH  Google Scholar 

  15. Khan NU, Arya KV, Pattanaik M (2010) An efficient image noise removal and enhancement method. In: Proceedings of 2010 IEEE international conference on systems, man and cybernetics, (SMC 2010). Istanbul, Turkey, 10–13 October 2010, pp 3735–3740

  16. Khan NU, Arya KV, Pattanaik M (2012) Fuzzy based diffusion coefficient function in anisotropic diffusion for impulse noise removal. In: Proceedings of 8th Indian conference on vision, graphics and image processing, (ICVGIP 2012). Bombay, India, 16–19 December 2012, pp 3735–3740

  17. Perona P, Malik J (1990) Scale space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(8):629–639

    Article  Google Scholar 

  18. Pinho AJ, Almeida AB (1996) Figures of merit for quality assessment of binary edge maps. In: Proceedings of 3rd IEEE international conference on image processing, (ICIP 96), vol 3. Lausanne, Swittzerland, 16–19 September 1996, pp 591–594

  19. Smolka B, Chydzinski A (2005) Fast detection and impulsive noise removal in color images. J Real-Time Imaging 11(5):389–402

    Article  Google Scholar 

  20. Smolka B (2010) Peer group switching filter for impulse noise reduction in color images. Pattern Recogn Lett 31(6):484–495

    Article  MathSciNet  Google Scholar 

  21. Tschumperle D, Deriche R (2005) Vector-valued image regularization with PDEs: a common framework for different application. IEEE Trans Pattern Anal Mach Intell 27(4):506–517

    Article  Google Scholar 

  22. Wang Y, Zhang L, Li P (2007) Local variance-controlled forward-and-backward diffusion for image enhancement and noise reduction. IEEE Trans Image Process 16(7):1854–1864

    Article  MathSciNet  Google Scholar 

  23. Wang S, Liu Q, Xia Y, Dong P, Luo J, Huang Q, Feng DD (2013) Dictionary learning based impulse noise removal via L1-L1 minimization. Signal Process 93:2696–2708

    Article  Google Scholar 

  24. Witkin AP (1983) Scale-space filtering. In: Proceedings of 8th international joint conference on artificial intelligence, pp 1019–1022

  25. Wu J, Tang C (2011) PDE-based random-valued impulse noise removal based on new class of controlling functions. IEEE Trans Image Process 20(9):2428–2438

    Article  MathSciNet  Google Scholar 

  26. Xiao Y, Zeng T, Yu J, Ng MK Restoration of images corrupted by mixed gaussian-impulse noise via L1-L0 minimization. Pattern Recogn 44:1708–1720

  27. You Y, Kaveh M (2000) Fourth-order partial differential equations for noise removal. IEEE Trans Image Process 9(10):1723–1730

    Article  MathSciNet  MATH  Google Scholar 

  28. Yu J, Wang Y, Shen Y (2008) Noise reduction and edge detection via kernel anisotropic diffusion. Pattern Recogn Lett 29:1496–1503

    Article  Google Scholar 

  29. Zheng K, Feng W, Chen H (2010) An adaptive nonlocal-means algorithm for image denoising via pixel region growing and merging. In: Proceedings of 2010 3rd international congress on image and signal processing. Yantai, China, 16–18 October 2010, pp 621–625

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Authors and Affiliations

  1. Department of Information and Communication Technology, ABV-Indian Institute of Information Technology and Management, Gwalior, 474015, India

    Nafis Uddin Khan, K. V. Arya & Manisha Pattanaik

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  1. Nafis Uddin Khan
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  2. K. V. Arya
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  3. Manisha Pattanaik
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Correspondence to Nafis Uddin Khan.

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Khan, N.U., Arya, K.V. & Pattanaik, M. Edge preservation of impulse noise filtered images by improved anisotropic diffusion. Multimed Tools Appl 73, 573–597 (2014). https://doi.org/10.1007/s11042-013-1620-8

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  • DOI: https://doi.org/10.1007/s11042-013-1620-8

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