Back
 JSIP  Vol.2 No.3 , August 2011
Non Linear Image Restoration in Spatial Domain
Abstract: In the present work, a novel image restoration method from noisy data samples is presented. The restoration was performed by using some heuristic approach utilizing data samples and smoothness criteria in spatial domain. Unlike most existing techniques, this approach does not require prior modelling of either the image or noise statistics. The proposed method works in an interactive mode to find the best compromise between the data (mean square error) and the smoothing criteria. The method has been compared with the shrinkage approach, Wiener filter and Non Local Means algorithm as well. Experimental results showed that the proposed method gives better signal to noise ratio as compared to the previously proposed denoising solutions. Furthermore, in addition to the white Gaussian noise, the effectiveness of the proposed technique has also been proved in the presence of multiplicative noise.
Cite this paper: nullB. Jalil, F. Eric and L. Olivier, "Non Linear Image Restoration in Spatial Domain," Journal of Signal and Information Processing, Vol. 2 No. 3, 2011, pp. 211-217. doi: 10.4236/jsip.2011.23029.
References

[1]   A. Bruckstein M. Lindenbaum and M. Fischer, “On Gabor Contribution to Image Enhancement. Pattern recognition,” Computer Methods and Programs in Biomedicine, Vol. 27, No. 1, 1994, pp. 1-8.

[2]   J. Malik and P. Perona, “Scale Space and Edge Detection Using Anisotropic Diffusion,” IEEE Transactions on Pattern Analysis, Vol. 12, No. 7, March 1990, pp. 629-639.

[3]   J. M. Morel L. Alvarez and P. L. Lions, “Image Selective Smoothing and Edge Detection by Nonlinear Diffusion,” Journal of Numerical Analysis, Vol. 29, No. 1, 1992, pp. 182-193.

[4]   L. Yaroslavsky, “Digital Picture Processing—An Introduction,” Springer-Verlag, Berlin, 1985.

[5]   R. Manduchi and C. Tomasi, “Bilateral Filtering for Gray and Color Images,” Proceedings of the Sixth International Conference on Computer Vision, Bombay, 4-7 January 1998, pp. 839-846.

[6]   D. Donoho and R. Coifman, “Wavelets and Statistics, Chapter Translation Invariant Denoising,” Springer-Verlag, Berlin, 1995, pp. 125-150.

[7]   D. Donoho, “Denoising by Soft-Thresholding,” IEEE Transactions on Information Theory, Vol. 41, 1995, pp. 613-627.

[8]   A. Alecu, A. Munteanu, L. Tessens and A. Pizurica, “Context Adaptive Image Denoising through Modeling of Curvelet Domain Statistics,” Journal of Electronic Imaging, Vol. 17, No. 3, 2008, p. 033021. doi:10.1117/1.2987723

[9]   J. Morel, A. Buades and B. Coll, “On Image Denoising Methods,” Technical Report CMLA, 2004.

[10]   J. M. Morel, A. Buades and B. Coll, “A Non-Local Algorithm for Image Denoising,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, 20-26 June 2005, pp. 60-65.

[11]   A. Reza and Y. Hawwar, “Spatially Adaptive Multiplicative Noise Image Denoising Technique,” IEEE Transactions on Image Processing, Vol. 11, No. 12, 2002, pp. 1397-1404. doi:10.1109/TIP.2002.804526

 
 
Top