Denoising of Images corrupted by Random noise using Complex Double Density Dual Tree Discrete Wavelet Transform

This paper presents removal of random noisenoise by complex double density dual tree discrete wavelet Transform. In general in images noise suppression is a particularly delicate and difficult task. A tradeoff between noise reduction and the preservation of actual image features has to be made in a way that enhances the relevant image content. The main properties of a good image denoising model are that it will remove noise while preserving edges and contours. However, wavelet coefficients of natural images have significant dependencies. For many natural signals, the wavelet transform is a more effective tool than the Fourier transform. The wavelet transform provides a multiresolution representation using a set of analyzing functions that are dilations and translations of a few functions (wavelets). In this paper we have evaluated & compared performances of Standard Double Density DWT(SDDDWT), Real Double Density Dual Tree (RDDDTDWT) and Complex Double Density Dual Tree DWT(CDDDTDWT). Simulation and experimental results demonstrate that the complex double density dual tree discrete wavelet transform (CDDDTDWT) outperforms a number of other existing wavelet transform techniques and it is particularly effective for the very highly corrupted images.