Application And Research On Theory Of Second Generation Wavelet Transforms In Image Denoising

With the development of science technique that makes great changes of information soiety, the multi-media technique becomes an important part of computer development in 21 centuries. People through various medium such as language, music, writing, sketch, picture, television to carry on the processing of information, deliver and saving. But usually the noise and signal keep both, the existence of the noise will make big influence to the processing of information, deliver and save. Therefore, how better deliver the noise, is a classic problem of the signal processing fields.In the late 1980s, Daubechies proposed a systematical approach of constructing compactly. Supported orthogonal wavelet, and Mallat presented multiresolution analysis and discrete wavelet transform. At presently, in the fields of wavelet, the lifting scheme is the key question, not only because of its generality and flexibility, but also since it allows efficient implementation. This paper mainly studied on the theory of wavelet transform based on the lifting scheme, and proposed the second wavelet transform based on the lifting scheme for the application in the fields of the signals and image. At the same time, its application in image de-noising was researched. Its mainly include:This thesis will analyze, compare and discuss the separate applications on different kinds of typical remove noise processing methods.In addition, focus on Lift Wavelet transform theory, pays much importance to lifting scheme, including how to construct traditional wavelets, and reveals the reason of flexibility of using lifting scheme in constructing traditional wavelets.The author analyzes its characteristic and takes its advantage to implement a new denoising mechanism. Also will traditional wavelets D4, 9/7, Haar, 5/3 after lifting with correspond of original wavelets at the different noise grade of the image and the pixel size up, carry on an experiment to the de-noising. Both theory analyzing and experiment result on non-stationary signal in order to certify the Wavelet transformremove noise method is superior to the typical ones.Detection on signal singularity point is also discussed in this thesis. Lift Wavelet this text also discussed lifting wavelet usage in other fields, such as: Detection on signal singularity point, Image fusion. Experiment enunciation, Lifting wavelet to all has a good performance in these aspects.