Complex Wavelet Transform Based Hyper Analytical Image Denoising by Bivariate Shrinkage Technique

International Journal of Computer Trends and Technology (IJCTT)          
© 2017 by IJCTT Journal
Volume-43 Number-3
Year of Publication : 2017
Authors : S.Sasipavan Kumar, Dr.T.Sreenivasulu Reddy
DOI :  10.14445/22312803/IJCTT-V43P122


S.Sasipavan Kumar, Dr.T.Sreenivasulu Reddy  "Complex Wavelet Transform Based Hyper Analytical Image Denoising by Bivariate Shrinkage Technique". International Journal of Computer Trends and Technology (IJCTT) V43(3):143-150, January 2017. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
Removal of noise is an important step in the image restoration process, but de-noising of image remains a challenging problem in recent research associated with image processing. De-noising is used to remove the noise from corrupted image, while retaining the edges and other detailed features as much as possible. This noise gets introduced during acquisition, transmission, reception, storage and retrieval processes. Here we retrieve de-noised image using Bivariate Shrinkage Technique. Out of many wavelet transforms here Discrete Wavelet Transform,Dual Tree Wavelet Transform and Hyper Analytical Wavelet transforms are implemented on different noisy images.Here the noisy image is assumed to be complex image and its real part and imaginary parts are separated. These are subjected to Bi-shrink filter separately into different stages of decomposition depending upon the severity of noise. The obtained de-noise image is compared with original image using different parametric measures like Peak Signal to Noise Ratio, Structural similarity Index measure, Covariance and Root mean square Error whose values are tabulated. The values of retrieved image obtained yields much better visual effect and hence this method is said to be a better one when compared with de-noising methods using Weiner Filter and various Local Adaptive Filters.

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Weiner Filter and various Local Adaptive Filters.