, 3(3), 11-16.'> Denoising of Images by Wavelets and Contourlets using Bi-Shrink Filter

Denoising of Images by Wavelets and Contourlets using Bi-Shrink Filter

S. Swarnalatha*, P. Satyanarayana**
* Associate Professor, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Tirupati, India.
** Professor, Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, Tirupati, India.
DOI : https://doi.org/


Denoising refers to the recovery of an image that has been contaminated by noise due to poor quality of image acquisition and transmission. Accordingly, there is a need to reduce the noise present in the image as a consequence to produce the denoised image. This paper presents Image denoising using Wavelet transforms and Contourlet transforms governed by bivariate shrinkage (Bi-shrink) filter techniques. The Wavelet transforms have the shift sensitivity and poor directionality that is shown by peak signal-to-noise ratio. In this paper, Translation Invariant Contourlet Transforms is proposed to overcome the limitations of wavelet transforms, hence to increase the peak signal-to-noise ratio. The results illustrate the efficacy of the proposed transform in terms of peak signal-to-noise ratio, execution time and visual quality of images.


Wavelet Transforms, Contourlet Transforms, Bi-variate Shrinkage, Translation Invariance, Gaussian Noise, Salt & Pepper Noise, Image Denoising.

How to Cite this Article?

Swarnalatha, S., and Satyanarayana, P. (2016). Denoising of Images by Wavelets and Contourlets Using Bi-Shrink Filter. i-manager's Journal on Image Processing, 3(3), 11-16.


[1]. Gang Liu, Jing Liu, Quan Wang and Wenjuan, (2012). “The Translation Invariant Wavelet-based Contourlet Transform for Image Denoising”. The Journal of Multimedia, Vol. 7, No. 3, pp. 254-261.
[2]. P.S. Hiremath, Prema T. Akkasaligar and Sharan Badiger, (2011). “Performance Comparison of Wavelet Transform and Contourlet Transform based methods for Despeckling Medical Ultrasound Images”. International Journal of Computer Applications, Vol. 26, No. 9, pp. 34-41.
[3]. Minh N. Do, Member, IEEE, and Martin Vetterli, (2005). “The Contourlet Transform: An Efficient Directional Multiresolution Image Representation”. IEEE Transactions on Image Processing, Vol. 14, No. 12, pp. 2091-2106.
[4]. Ivan W. Selesnick, Richard G. Baraniuk and Nick G. Kingsbury (2005). “The Dual-Tree Complex Wavelet Transform”. IEEE Signal Processing Magazine, Vol. 22, No. 6, pp. 123-151.
[5]. Levent S. Endur and Ivan W. Selesnick, (2002). “Bivariate Shrinkage with Local Variance Estimation”. IEEE Signal Processing Letters, Vol. 9, No. 12, pp. 438-441.
[6]. N. Kingsbury (2001). “Complex Wavelets for Shift Invariant Analysis and Filtering of Signals”. Applied and Comp. Harm. Anal.10, pp. 234-253.
[7]. K. P. Soman and K. L. Ramachandran, (2005). Insight into WAVELETS form Theory to Practice. Second Edition, Prentice Hall of India.
[8]. Anil K. Jain (2004). Fundamentals of Digital Image Processing. Pearson Education.
[9]. http.//www.imageprocessingplace.com
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