Image Denoising Analysis Using Adaptive Subband Thresholding and Wavelet Packet

Abstract

In image processing, removal of noise without blurring the image edges is a difficult problem. Aiming at orthogonal wavelet transform and traditional thresholds shortage, a new adaptive threshold image de-noising method which is based on wavelet packet transform, decomposition level and sub band coefficients is proposed. 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. The proposed method describes a new method for suppression of noise in image by fusing the wavelet Packet (WP) transform (WPT) along with optimal wavelet basis (OWB) (WPTOWB) for image decomposition. Shannon entropy is implemented to produce the optimal wavelet basis function. Then, for each wavelet subband, an adaptive threshold value to which a multiplying factor (α) is included making the threshold value dependent on decomposition level and subband’s statistical parameters are estimated. From the experiment result, we see that compared with traditional methods, this method can not only effectively eliminate noise, but can also well keep original images information and the quality after image de-noising.