Analysis of Wavelet Denoising of a Colour Image with Different Types of Noises
P.V.Lakshman Kumar, Sandeep Kumar Agarwal · International Journal of Signal Processing Image Processing and Pattern Recognition · 2015
There are various types of noises that affect quality of an image such as Salt-andpepper noise, Poison noise, Gaussian noise, Speckle noise etc. Wavelet is a powerful tool for denoising a variety of signals.Here a White Flower image 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 hard thresholding method is used to remove the noisy coefficients by fixing the optimum thresholding value.In this paper, analysis of a colored image is carried out with four different noises at zero mean that are applied on the image to produce noisy images.Residual image is obtained from the original and noisy image & its statistical parameters such as mean, median, mode, standard deviation, mean absolute deviation, median absolute deviation are calculated.In order to enhance the quality of the noisy images, performance parameters of denoised images must be estimated.The comparison between noisy and 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).