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.
Periodicity:July - September'2016


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.


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