Complex Discrete Wavelet Transform Based Image Denoising using Thresholding
D. Srinivasulu Reddy · 2013
Wavelet Techniques can be applied successfully in various signal and image processing techniques such as image denoising, segmentation and motion estimation. Complex Discrete Wavelet Transform (CDWT) has significant advantages over real wavelet transform for certain signal processing problems .CDWT is a form of discrete wavelet transform, which generates complex coefficients by using a dual tree of wavelet filters to obtain their real and imaginary parts. This Paper describes the application of complex wavelets for denoising the corrupted images and the results are compared with normal Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT).The algorithm exhibits consistency in denoising for different Signal to Noise Ratios( SNRs). Equivalently, for specially designed sets of filters, the wavelet associated with the upper DWT can be an approximate Hilbert transform of the wavelet associated with the lower DWT. When designed in this way, the dual-tree complex DWT is nearly shift-invariant, in contrast with the critically-sampled DWT. Moreover, the dual-tree complex DWT can be used to implement 2D wavelet transforms where each wavelet is oriented, which is especially useful for image processing. (For the separable 2D DWT, recall that one of the three wavelets does not have a dominant orientation.) The dual-tree complex DWT outperforms the critically sampled DWT for applications like image denoising and enhancement.