Performance analysis of Wavelet Filters for Image Denoising

In diverse fields from planetary science to molecular spectroscopy and medical imaging to satellite imaging, scientists are faced with the problem of recovering original images from incomplete, indirect and noisy images. The conventional Fast Fourier Transform (FFT) based image denoising method is essentially a low pass filtering technique in which edge is not as sharp in the reconstruction as it was in the original. The drawback of the FFT is the fact that the edge information is spread across frequencies because of the FFT basis functions, not being localized in time or space and hence low pass-filtering results in the smearing of the edges. But the localized nature of the wavelet transform both in time and space results in denoising with edge preservation. In this paper, the performance of image denoising algorithm using Discrete Wavelet Transform (DWT) is experimentally analyzed for various Wavelet filters, such as, Haar, Daubechies, Symlet, Discrete Meyer and Coiflets, for second level of DWT decomposition, for Gaussian, Speckle and Salt & Pepper noise added facial such as Lena, Barbara, and two industrial CT images.