Image Denoising Based On The Dual Tree Complex Wavelet Tansform

Image signal if often influenced by many kinds of noise when collected, acquisited, encoded and transmitted; and the image degradation maybe impair the quality of the image information processing, transmission and storage. Therefore it is a very important work to reduce image noise. The processing in order to reduce noise, and to improve the quality of image is called Image denoising. We are pursuing an effective method not only can it reduce the noise, but also retain the image edge information. In recent years, with the development of the wavelet theory, diserete wavelet transform(DWT) has been widely used in image denoising with its good time-frequency localization characteristics, scale characteristics and direction characteristics.Although the discrete wavelet transform denoising has been widely applied, it has two limitations, mainly in two aspects. Firs,the lack of translation invariance:this means that the input signal with a very small shift can lead to various scales in the wavelet coefficients so that energy distribution will have significant changes. Second, lack of direction selectivity:diserete wavelet transoform coeffieients reveal only threes spatial orientations. (horizontal, vertical, diagonal), so the direction selectivity is limited. In order to overeome the shortcoming of the commonly—used denoisng methods, the image denoising method based on dual tree complex wavelet trnasoform(DT-CWT) is proposed.The paper uses Dual Tree Complex Wavelet Transform for image denoising to overcome the shortage of DWT above-mentioned. The main work can be summarized as ofllows:(1) We discussed the general principles of wavelet denoising, wavelet denoising introduced several methods, focusing on learning the four types of classical discrete wavelet transform based denoising algorithm:wavelet transform modulus maxima denoising, wavelet transform correlation between scale denoising, wavelet shrinkage threshold denoising method and the translation invariant wavelet thresholding denoising method. And the four denoising algorithms were compared.(2) We introduced the dual tree complex wavelet transform principle and character. Dual tree complex wavelet transform provides approximate shift invariance, good directional selectivity, at the same time, it also has perfect reconstruction feature. Dual tree complex wavelet transform in the direction of each layer with the selective production of six sub-band, respectively. Dual tree complex wavelet transform will be applied to image denoising, and it can better represent the image edge and texture features, which has smaller waves better denoising effect.(3) We presents an algorithm in image denoising based on multi-BKF using Dual-Tree Complex Wavelet.This method incorporates both interscale and intrascale wavelet coefficients into the model of the multi-BKF and it sufficiently takes into accout the relevance of interscale and intrascale coefficients. New corresponding nonlinear threshold functions are derived from the models using MAP estimation theory. Ultimately, we obtain the denoised image by computing the inverse transform using the modified coefficients. At the same time, the Dual-Tree Complex Wavelet is discussed(DT-CWT); compared with the traditional discrete Wavelete transform, DT-CWT has the properties of approximate shift invariance and more directionality. The experimental results show that the algorithm performs better than other traditional de-noising algorithms.