An Image Denoising Framework Using Wavelet Shrinkage and Dt-cwt

Non-stationary signal processing applications use standard nonredundant DWT (Discrete Wavelet Transform) 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 .The project presents that noise reduction of enhanced images using Dual tree complex wavelet transform double density dual tree wavlet transform filter. So the denoising process will be handled to reduce distortion under Dual tree complex wavelet transform domain.The measures used to assess the quality of the denoised image are the PSNR (Peak Signal to Noise Ratio). By denoising a noisy image with a standard deviation noise σ = 40, with the use of the threshold verifying T2logN()=σe and the Haar wavelet and DTCWT, we obtain a PSNR of 27.11 and 37.264 respectively.