Wavelet Packet Denoising Algorithm Based on Correctional Wiener Filtering

Abstract

Image denoising is an important step in the field of image processing. In order to improve the quality of the degraded images, based on wavelet threshold denoising algorithm put forward by Donoho, the theory of Wiener filtering is analyzed and a denoising method using wavelet packet transforms based on the Wiener filtering is proposed. Firstly, the noisy image is processed by the correctional Wiener filtering and the noise standard deviation is calculated by the remaining signal of Wiener filter to regard as the threshold of wavelet packet transforms. Then the image is decomposed into the low frequency part and high frequency part by using wavelet packet transform and the wavelet packet tree coefficients are processed with soft threshold by using the level dependent adaptive threshold. Finally, the denoising image is acquired by using wavelet packet inverse transform. The results indicate that, compared with denoising method on wavelet packet adaptive threshold, the Peak Signal-to-Noise Ratio (PSNR) gain of the proposed algorithm has reached 8.8 dB when the noise variance is 0.01. The algorithm is more efficient in noise removal and edge reservation for all the noise images with different noise variances.