Complex discrete wavelet transform-based image denoising

Abstract EN

Dual tree complex discrete wavelet transform is implemented for demising as an important image processing application.

Two wavelet trees are used, one generating the real part of the wavelet coefficients tree and the other is 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 dimensional signal.

This paper introduces firstly a proposed method of computing one and two-dimensional 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 simulation.