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Fusion Based Gaussian noise Removal in the Images using Curvelets and Wavelets with Gaussian Filter
Naga Sravanthi Kota, G. Umamaheswara Reddy
Pages - 456 - 468     |    Revised - 01-09-2011     |    Published - 05-10-2011
Volume - 5   Issue - 4    |    Publication Date - September / October 2011  Table of Contents
Gaussian Filtering, Wavelet Transform, Curvelets Transform, Image Fusion, Denoising
Curvelets denoise approach has been widely used in many fields for its ability to obtain high quality result images.Curvelet transform is superior to wavelet in the expression of image edge, such as geometry characteristic of curve, which has already obtained good results in image denoising. However artifacts those appear in the result images of Curvelets approach prevent its application in some fields such as medical image. This paper puts forward a fusion based method because certain regions of the image have the ringing and radial stripe after Curvelets transform. The experimental results indicate that fusion method has an abroad future for eliminating the noise of images. The results of the algorithm applied to ultrasonic medical images also indicate that the algorithm can be used efficiently in medical image fields.
CITED BY (10)  
1 Helonde, M. R. P., & Joshi, M. R. Image Fusion Based on Medical Images Using DWT and PCA Methods.
2 Kumar, K. S. (2015). Analysis and classification of wound images for healing assessment.
3 Lakshmi, A. S., & SN, N. S. Cardiac Cycle Phase Estimation in 2-D Echocardiographic Images Using SVM.
4 Helonde, M. R. P., & Banubakode, M. S. S. A Study on Various Image Fusion Techniques.
5 Kaur, D., & Mann, P. S. medical image fusion using gaussian filter, wavelet transform and curvelet transform filtering.
6 Seddik, H. (2014). A new family of Gaussian filters with adaptive lobe location and smoothing strength for efficient image restoration. EURASIP Journal on Advances in Signal Processing, 2014(1), 1-11.
7 Kumar, S. S. (2014). Computer aided diagnosis of liver tumours from CT images.
8 Bhosale, B. (2014). Curvelet Based Multiresolution Analysis of Graph Neural Networks. International Journal of Applied Physics and Mathematics, 4(5), 313.
9 Kaur, D., & Mann, P. S. (2013). Hybrid Transform Domain Algorithm for Medical Image Fusion. International Journal for Science and Emerging Technology with Latest Trends (IJSETT), Vo lu me, 8, 23-27.
10 Umamaheswari, J., & Radhamani, D. G. (2012). Hybrid Denoising Method for Removal of Mixed Noise in Medical Images. International Journal of Advanced Computer Science and Applications, 3(5).
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 M V S Grace Chang, Bin Yu. “Adaptive wavelet thresholding for image denoising and compression”, IEEE Transactions on Image Processing, Vol.9 (9), Sep 2000, pp.1532-1546.
2 E.J.Candes, “Ridgelets: Theory and applications, Department of Statistics”, Stanford University, pp.1998, 23-38.
3 E.J.Candes,” Monoscale Ridgelets for the Representation of Image with Edges”, Department of Statistics, Stanford University, Stanford,CA,1999, pp.1-26.
4 J.L. Startck, E.J. Candes, D.L. Donoho, “The curvelet transform for image denoising,” IEEE Transactions on Image Processing, Vol.11 (6), 2002, pp.670-684.
5 E.J.Candes and D.Donoho, “Curvelet: a surprisingly effective non adaptive representation for object with edges”, Proceeding of Curves and Surfaces IV, France, 1999, pp.105-121.
6 David L.Donoho & Mark R.Duncan,”Digital Curvelet Transform: Strategy, Implementation and Experiments”, Department of statistics, Stanford University, Nov 1999, pp.1-20.
7 J.Fadili, J.L.Starck, “Curvelets and Ridgelets”, France, Oct 24, 2007, pp.1-30.
8 E.Candes, L.Demanet, D.Donoho, L.Ying,”Applied and Computational Mathematics”, Department of Stanford university, Stanford, Mar 2006, pp.1-44.
9 Anil A.Patil, J.Singhai,” Image Denoising Using Curvelet Transform: an approach for edge preservation”, Journal of scientific and Industrial Research, vol.69, Jan 2010, pp.34-38.
10 S.G.Mallat,”A theory for Multiresolution signal decomposition: The wavelet representation”, IEEE transactions on Pattern recognition, vol.11, pp. 674-693, 1989.
11 J.M.Johnstone, D.L.Donoho,”Adapting to smoothness via wavelet shrinkage”, Journal of the Statistical association”, 1995, vol.90, pp.1200-1224.
12 D.L.Donoho,”Denoising by soft thresholding”, IEEE transactions on Information theory, 1995, vol.41, pp.613-627.
13 D.Donoho and I.Johnstone,”Minimax estimation via wavelet shrinkage”, Annals of statistics, 1998, vol.26, pp.879-921.
14 B.Vidakovic,”Statistical modeling by wavelets”, John Wiley & Sons, NY, 1999.
15 I. Daubechies, “Wavelet transforms and orthonormal wavelet bases, different perspectives on wavelets,” in Proceedings of the Symposia in Applied Mathematics, Vol. 47, pp. 1–33, American Mathematical Society,1993, San Antonio, Tex, USA.
16 D. L. Donoho, “Ridge functions and orthonormal ridgelets,” Journal of Approximation Theory, 2001, Vol. 111, no. 2, pp. 143–179.
17 W. Fourati, F. Kammoun, and M. S. Bouhlel, “Medical image denoising using wavelet thresholding,” Journal of Testing and Evaluation,2005, Vol. 33, no. 5, pp. 364–369.
18 M. Do and M. Vetterli, “Image denoising using orthonormal finite ridgelet transform,” in Wavelet Applications in Signal and Image Processing,2003,vol. 4119 of Proceedings of SPIE, pp. 831–842.
19 L. Demanet, “The curvelet Organization,” http://www.curvelet.org/software.html.
Miss Naga Sravanthi Kota
Associate Professor G. Umamaheswara Reddy