Study and Comparison of MRI Image Denoising using Dual-Tree Complex DWT and Double- Density Dual-Tree Complex DWT

摘要

DWT (Discrete Wavelet Transform) is used for image denoising which is very powerful tool. But it suffers from shift sensitivity, absence of phase information, and poor directionality. To remove out these limitations, many researchers developed extensions to the standard DWT such as WP (Wavelet Packet Transform), and SWT (Stationary Wavelet Transform). These extensions are highly redundant and computationally intensive. Complex Wavelet Transform (CWT) is also an impressive option, complex-valued extension to the standard DWT. There are various applications of Redundant CWT (RCWT) in an image processing such as Denoising, Motion estimation, Image fusion, Edge detection, and Texture analysis. In this work, the focused application is the image denoising using two innovative techniques and the images are considered which are corrupted by a random noise. In this paper, first two sections explain about introduction to the topic and regarding wavelet transform domain. Third section gives an idea about basics concepts of the system. Forth section illustrates the proposed systems. Last section gives results and discussion. Here promising results are compared with DWT extensions namely, Dual- Tree Complex DWT (DTCWT) and Double-Density Dual-Tree Complex DWT (DDDTCWT).