Arun Dixit

Work place: Department of Computer science & Engineering, M.I.T.S., Gwalior, India

E-mail: adixit231@gmail.com

Website:

Research Interests: Image Processing, Image Manipulation, Image Compression

Biography

Arun Dixit is born in India in 1990. He is currently M.Tech scholar in computer science and engineering department at Madhav Institute of Technology and Science, Gwalior, India. Prior to it, he was lecturer in Apollo Institute of Technology, kanpur, India. He has completed B.Tech in computer science and engineering from Maharana Pratap Engineering College, Kanpur, India in 2011. His research area includes Wavelet processing, Image denoising, Image enhancement and Meta-heuristic optimization.

Author Articles
A Comparative Study of Wavelet Thresholding for Image Denoising

By Arun Dixit Poonam Sharma

DOI: https://doi.org/10.5815/ijigsp.2014.12.06, Pub. Date: 8 Nov. 2014

Image denoising using wavelet transform has been successful as wavelet transform generates a large number of small coefficients and a small number of large coefficients. Basic denoising algorithm that using the wavelet transform consists of three steps – first computing the wavelet transform of the noisy image, thresholding is performed on the detail coefficients in order to remove noise and finally inverse wavelet transform of the modified coefficients is taken. This paper reviews the state of art methods of image denoising using wavelet thresholding. An Experimental analysis of wavelet based methods Visu Shrink, Sure Shrink, Bayes Shrink, Prob Shrink, Block Shrink and Neigh Shrink Sure is performed. These wavelet based methods are also compared with spatial domain methods like median filter and wiener filter. Results are evaluated on the basis of Peak Signal to Noise Ratio and visual quality of images. In the experiment, wavelet based methods perform better than spatial domain methods. In wavelet domain, recent methods like prob shrink, block shrink and neigh shrink sure performed better as compared to other wavelet based methods.

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