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Image Denoising using Curvelet: an Approach based on Average Fusion

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IJCA Special Issue on Advanced Computing and Communication Technologies for HPC Applications
© 2012 by IJCA Journal
ACCTHPCA - Number 5
Year of Publication: 2012
Authors:
S. Sukumaran
M. Shanmugasundaram

S Sukumaran and M Shanmugasundaram. Article: Image Denoising using Curvelet: an Approach based on Average Fusion. IJCA Special Issue on Advanced Computing and Communication Technologies for HPC Applications ACCTHPCA(5):38-42, July 2012. Full text available. BibTeX

@article{key:article,
	author = {S. Sukumaran and M. Shanmugasundaram},
	title = {Article: Image Denoising using Curvelet: an Approach based on Average Fusion},
	journal = {IJCA Special Issue on Advanced Computing and Communication Technologies for HPC Applications},
	year = {2012},
	volume = {ACCTHPCA},
	number = {5},
	pages = {38-42},
	month = {July},
	note = {Full text available}
}

Abstract

The most significant task of image processing is to reduce noise which is commonly found in images. In recent years, technology is being improved to analyze the images to get better quality. Since the image gets loss of edge feature and detail information during the process of de-noise, this paper attempts to present and compare a new method based on curvelet transform using image fusion. Results show that this approach has a broad future for removing noise as well as preserving edges of image.

References

  • 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
  • D. L. Donoho, "Denoising by Soft-Thresholding", IEEE Transactions on Information Theory 41, 613-627, 1995
  • David L. Donoho & Mark R. Duncan, "Digital Curvelet Transform: Strategy, Implementation and Experiments", Department of statistics, Stanford University, Nov 1999, pp. 1-20
  • E. Candes, L. Demanet, D. Donoho, L. Ying, "Applied and Computational Mathematics", Department of Stanford university, Stanford, Mar 2006, pp. 1-44
  • E. J. Candes and D. Donoho, "Curvelet: a surprisingly effective nonadaptive representation for object with edges", Proceeding of Curves and Surfaces IV. France,pp. 105-121. 1999
  • E. J. Candes, L. Demanet, D. L. Donoho et al. "Fast Discrete Curvelet Transforms. Applied and Computational Mathematics", California Institute of Technology,pp. 1~43, 2005
  • Elad M, Aharon M, "Image Denoising via Sparese and Redundant Representation over Learned Dictionaries", IEEE Transaction of Image Processing, 2006, Vol 15 No 12
  • J. Fadili, J. L. Starck, "Curvelets and Ridgelets", France, Oct 24, 2007, pp. 1-30
  • Jean-Luc Starck, Mai K. Nguyen, "Fionn Murtagh. Wavelets andcurvelets for image deconvolution:a combined pproach", Signal Processing 83 (2003) 2279 – 2283
  • JIANG Tao and ZHAO Xin, "Research and application of image denoising method based on curvelet transform" The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part 2. Beijing 2008
  • K. Berkner, R. O. Wells Jr, "A Correlation-Dependent Model for Denoising via Nonorthogonal Wavelet Transforms", Computational Mathematics Laboratory, Rice Univ. , Technical Reports 98-07
  • Liyong Ma, Member, IAENG, Jiachen Ma and Yi Shen, "Pixel Fusion Based Curvelets and Wavelets Denoise Algorithm", Engineering Letters, 14:2, EL_14_2_16 (Advance online publication: 16 May 2007)
  • M. A. Abidi and R. C. Gonzalez, "Data Fusion in Robotics and Machine Intelligence" New York: Academic, 1992
  • P. K. Varshney, "Scanning the special issue on data fusion," Proc. IEEE, vol. 85, pp. 3–5, Jan. 1997
  • Strack J L, Elad M, Donoho D L, "Image Decomposition via the Combination of Sparse Representation and a Variational Approach", IEEE Transaction of Image Processing, 2005, Vol 14, Issue 10 , pp. 1570-1582
  • T. A. Wilson, S. K. Rogers, and L. R. Myers, "Perceptual-based hyperspectral image fusion using multiresolution analysis," Opt. Eng. , vol. 34, no. 11, pp. 3154–3164, 1995