Comparative Analysis of DWT, Weiner Filter and Adaptive Histogram Equalization for Image Denoising and Enhancement

  IJCOT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© - August Issue 2013 by IJCTT Journal
Volume-4 Issue-8                           
Year of Publication : 2013
Authors :Rajwant Kaur , Sukhpreet Kaur

MLA

Rajwant Kaur , Sukhpreet Kaur"Comparative Analysis of DWT, Weiner Filter and Adaptive Histogram Equalization for Image Denoising and Enhancement "International Journal of Computer Trends and Technology (IJCTT),V4(8):2545-2551 August Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract:- This paper presents image denoising and Gaussian noise reduction model using different techniques including discrete wavelet transform, adaptive histogram equalization and weiner filter. Wavelets are the latest research area in the field of image processing and enhancement. The results show a comparison of three mentioned techniques showing an improved visual quality in wavelet transform technique than other two older techniques and is effective for removing the Gaussian noise corrupted image alone. Wavelet analysis represents the next logical step: a windowing technique with variable-sized regions. Wavelet analysis allows the use of long time intervals where we want more precise low-frequency information, and shorter regions where we want high-frequency information. Generally biomedical image is corrupted by Gaussian noise. So image de-noising has become a very essential exercise all through the diagnose. 2-D Discrete wavelet transform have been studied and an algorithm is developed to perform image denoising for Gaussian noise corrupted images using discrete wavelet transform. Results have been obtained using PSNR and MSE for three techniques. Processing time for different techniques implementation on MATLAB has also been framed. Gaussian noise reduction is another main criterion for determining the image quality objectively.

 

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Keywords : — Discrete Wavelet Transform, Image Denoising and enhancement, Gaussian Noise