In-depth Analysis of Wavelet Transform based Denoising Scheme for Smooth and Textured Images Corrupted with Gaussian Noise

Image denoising involves the manipulation of the image data to produce a clear and high quality image. Selection of the denoising algorithm is depends on the types of images and applications area of images. Hence, it is necessary to have knowledge about the noise present in the image so as to select the appropriate denoising algorithm. Wavelet based approach is Nobel approach for denoising smooth and textured images corrupted with Gaussian noise. This paper proposed the wavelet based approach with level depending threshold calculated by modified ‘sqtwolog’ method (universal method) at each scale on the images corrupted with Gaussian noise and performs their in-depth study by considering five major wavelet families like Haar, Daubecheis, Coiflets, Symlets and Biorthogonals. The noisy wavelet coefficients are threshold by Soft Threshold method. The edge preservation and sparse representation abilities of wavelet transform is utilized. A quantitative measure of comparison between original image and denoised image is provided by the PSNR (peak signal to noise ratio) for the smooth and textured images. General Terms Image Processing, Gaussian Noise.