Using Adaptive Diffusion Coefficients in Partial Diffusion Equation for Image Noise Reduction
Subject Areas : electrical and computer engineeringH. hassanpour 1 , M. nikpour 2
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Keywords: Diffusion coefficientpartial diffusion equation,
Abstract :
This paper proposes a new approach for image noise reduction using partial diffusion equation (PDE). Diffusion coefficient is an important parameter in PDE for image noise reduction. This parameter affects the noise reduction results and quality of edges in the denoised image. The existing PDE-based image denoising techniques experimentally adjust the diffusion coefficient. This paper proposes a new approach to adaptively adjust the diffusion coefficient. The proposed approach was applied on a number of standard images to evaluate its performance. The results indicate that the proposed approach outperform the existing PDE-based image denoisng techniques.
[1]H. Hassanpour, E. Nadernejad, and H. Miar, "Image restoration using a PDE-based approach," Int. J. of Engineering- Trans. B: Applications, Ar., vol. 20, no. 3, pp. 225-236, Dec. 2007.
[2]E. Nadernejad and H. Hassanpour, "A comparison and analysis of different PDE-based approachesfor image enhancement," in Proc. Int. Conf. on Signal Processing and Communication Systems ICSPCS, Australia, Dec. 2007.
[3]R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, 2004.
[4]J. Weickert, B. M. ter Haar Romeny, and M. Viergever, "Efficient and reliable schemes for nonlinear diffusion filtering," IEEE Trans. on Image Processing, vol. 7, no. 3, pp. 398-410, Mar. 1998.
[5]P. Perona and J. Malik, "Scale-space and edge detection using anisotropic diffusion," in Proc.IEEE Computer Society Workshop on Computer Vision, pp. 16-27, 20 Dec. 1987.
[6]I. Pollak, A. S. Willsky, and H. Krim, "Image segmentation and edge enhancement with stabilized inverse diffusion equations," IEEE Trans. on Image Processing, vol. 9, no. 2, pp. 256-266, Feb. 2000.
[7]P. Perona and J. Malik, "Scale-space and edge detection using anisotropic diffusion," IEEE Trans. on Pattern Analysis and Machine Intel., vol. 12, no. 7, pp. 629-639, Jul. 1990.
[8]S. K. Weeratunga and C. Kamath, "A comparison of PDE-based non-linear anistropic diffusion techniques for image denoising," Image Processing: Algorithms andSystems II, SPIE Electronic Imaging,Santa Clara, US, Jan. 2003.
[9]J. Weickert, B. M. ter Haar Romeny, and M. Viergever, "Efficient and reliable schemes for nonlinear diffusion filtering," IEEE Trans. on Image Processing, vol. 7, no. 3, pp. 398-410, Mar. 1998.
[10]E. Nadernejad and H. hassanpour, "Evaluating diffusion-based image de-noising techniques," inProc. Int. Conf. on Intelligent & Advance Systems ICIAS, pp. 565-570, Kuala Lumpur, Malaysia, 25-28 Nov. 2007.
[11]W. K. Pratt, Digital Image Processing, Wiley, New York, NY, USA, 1978.
[12]Y. Yu and S. Acton, "Speckle reducing anisotropic diffusion," IEEE Trans Image Process, vol. 11, no. 11, pp. 1260-1270, Nov. 2002.
[13]A. G. Weber, The USC-SIPI Image Database, http://sipi.usc.edu/services/database/Database.html, accessed in Nov. 2007.
