Complex Discrete Wavelet Transform-Based Image Denoising
AbstractDual tree complex discrete wavelet transform is implemented for denoising as
an important image processing application. Two wavelet trees are used, one
generating the real part of the wavelet coefficients tree and the other generating the
imaginary part tree.
A general computer program computing two dimensional dual tree complex
wavelet transform is written using MatLab V.7.0. for a general (NxN) two
This paper introduces firstly a proposed method of computing one and twodimensional
dual tree complex wavelet transform .The proposed method reduces
heavily processing time for decomposition of image keeping or overcoming the
quality of reconstructed images. Also, the inverse procedures of all the above
transform for multi- dimensional cases verified.
Secondly, many techniques are implemented for denoising of gray scale image.
A new threshold method is proposed and compared with the other thresholding
methods. For hard thresholding, PSNR gives (13.548) value while the PSNR was
increased in the proposed soft thresholding, it gives (14.1734) PSNR value when
the noise variance is (20).
Denoising schemes are tested on Peppers noise image to find its effect on
denoising application. The noisy version has SNR equals to (11.9373 dB), the
denoising image using WT has SNR equals to (17.4661 dB), the denoising image
using SWT has SNR equals to (18.1459 dB), the denoising image using WPT has
SNR equals to (19.3640 dB), the denoising image using Complex Discrete
Wavelet Transform has SNR equals to (21.9138 dB) using hard threshold and has
SNR equals to (22.1393 dB) using soft threshold. Matlab V.7.0 is used for
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