Image Denoising by Curvelets

G. Jagadeeswar Reddy*, T. Jaya Chandra Prasad**
*ECE Dept., SVIST, Kadapa, Andra Pradesh, India.
**ECE Dept., RGMCET, Nandyal, Kurnool, Andra Pradesh, India.
***ECE Dept., JNTUCE, Andra Pradesh, India.
Periodicity:August - October'2008
DOI : https://doi.org/10.26634/jfet.4.1.581

Abstract

The problem of recovering an image from noisy data arises in many different areas of scientific investigation and medical imaging. The traditional methods behave poorly when the object to recover has edges.

A new system of representation, namely, the curvelets, was developed over several years in an attempt to break an inherent limit plaguing wavelet denoising of images. The author(1) and standard images were denoised using both wavelet and curvelet transforms and results are presented in this paper. It has been found that the  curvelet reconstructions exhibit higher perceptual quality than wavelet-based reconstructions, offering visually sharper images and, in particular, higher quality recovery of edges and of faint linear and curvilinear features. Existing theory for curvelet transform suggests that this new approach can outperform wavelet methods in certain image reconstruction problems, such as image denoising and compression.

Keywords

Denoising, Wavelet, Curvelet, Transform, Anisotropic, Parabolic.

How to Cite this Article?

G. Jagadeeswar Reddy, T. Jaya Chandra Prasad and M. N. Giriprasad (2008). Image Denoising By Curvelets. i-manager’s Journal on Future Engineering and Technology, 4(1), 63-68. https://doi.org/10.26634/jfet.4.1.581

References

[1]. R.C. Gonzalez and R.E. Woods, “Digital Image Processing”.
[2]. M. Raghuveer Rao and S. Ajit Bopardikar, “Wavelet Transforms, Introduction to Theory and Applications”.
[3]. J. Shapiro, Embedded image coding using zerotrees of wavelet coefficients. IEEE Transactions on Signal Processing December 1993;41(12):3445-3462.
[4]. I.K. Fodor and C. Kamath, (2003). Denoising through Wavelet Shrinkage: An Empirical Study. SPIE Journal on Electronic Imaging. Vol. 12(1):151-160.
[5]. M. Malfaint and D. Roose.(1997). Wavelet-based image denoising using a Markov random field a priorimodel. IEEE Transactions on Image Processing. Vol. 6(4):549-565.
[6]. E.J. Candes and D.L. Donoho, “Curvelets” [online] available://www.stat.stanford.edu/~donoho/Reports/199 9/curvelets.pdf,1999.
[7]. E.J. Candes and D.L. Donoho, “Curvelets A Surprisingly Effective Nonadaptive Representation For Objects with Edges”, in Saint-Malo Proceedings
[8]. D.L. Donoho and M.R. Duncan, “Digital Curvelet Transform: strategy, Implementation and Experiments” [online] available: http://wwwstat. Stanford.edu/~donoho/Reports/2000 /curvesoft.pdf.
[9]. G. Jagadeeswar Reddy, T. Jayachandra Prasad and M.N. Giriprasad, in proceedings of iconADELCO February 2007
[10]. Jean-Luc Starck, Emmanuel J. Candes, and David L. Donohoo,”The Curvelet Transform for Image Denoi s ing” , in IEEE Transact ions on Image Processing,June 2002.
[11]. R.Sivakumar, “Denoising of Computer Tomography Images using Curvelet Transform”, in ARPN Journal of Engineering and Applied Sciences,February 2007.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.