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Analysis of Wavelet Denoising with Different Types of Noises

Author : Kishan Shivhare and Gaurav Bhardwaj

Pages : 2212-2217
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There are several types of noises those affect quality of an image such as Salt & pepper noise, Poisson noise, Gaussian noise, Speckle noise etc. Wavelet is a powerful tool for denoising a variety of signals. Here an image of a college building has been taken for denoising purpose with the help of HAAR Transform. The noisy image is first decomposed into five levels to obtain different frequency bands. Then first soft and hard thresholding methods are saperately used to remove the noisy coefficients by fixing the optimum thresholding value at 0.15 and then both of thresholdings are used in hybrid manner. In this paper, analysis of a colored image is carried out with four different noises at zero mean and at 0.02 variance that are applied on the image to produce noisy images. In order to enhance the quality of the noisy images, performance parameters of denoised images must be estimated. The comparison between different denoised image is taken in terms of MSE (mean square error), PSNR (peak signal to noise ratio), RMSE (root mean square error), SNR (signal to noise ratio) and SSIM (structural similarity index).

Keywords: Salt & Pepper noise, Speckle noise, Gaussian noise, Poisson noise, Discrete Wavelet Transform, Soft thresholding, Hard thresholding, SNR, PSNR, SSIM, MSE, RMSE.

Article published in International Journal of Current Engineering and Technology, Vol.6, No.6 (Dec-2016)

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