An Efficient Algorithm for Removal of Noise of Medical Images Using Complex Double Density Wavelet

Abstract

In digital image processing the wavelet transforms are used extensively because of it uses a set of filters for analysis and synthesis. The separable wavelet transform suffers from the problem of lacking shift invariance property and in multiple dimensions it cannot distinguish between orientations because of this the extensions of wavelet transform are required. Here we are proposing an extension of wavelet transform which uses both the properties of double density wavelet transform and dual tree complex wavelet transform. The double-density DWT is an improvement upon the critically sampled DWT with some properties such as it has one scaling function and two distinct wavelets, which are designed to be, offset from one another by one half and it is also shift invariant. This Paper uses complex double density wavelet transform for removal of noise and then determines Peak Signal to noise ratio (PSNR) and Root mean square error (RMS).