The Application Research Of Wavelet And Subdivision Scheme In Image Processing

In the late 1980s,Daubechies proposed a systematical approach of constructing compactly supported orthogonal wavelet and Mallat presented multiresolution analysis and fast discrete wavelet transform. Wavelet analysis have been applied to or involved in all fields of information domain and is internationally recognized as the most new tool of time-frequency analysis. The successful application of wavelet made people full of passion to explore the new theories and methods. Lifting wavelet was proposed in the middle of the 1990's. It is one of the best methods and a new method of realizing wavelet construction and transform. Lifting scheme had been adopted by ISO and it caused the birth of the Second Wavelet. Subdivision wavelet is the classical representation of the second generation wavelet. This paper takes importance of the applied research in the image processing based on wavelet and subdivision scheme.Because image denoising based on wavelet transform is an important method in image denoising,the paper summaries the basic image denoising methods based on single wavelet and each method's advantages and defects.This paper makes use of the relation of the wavelet coefficients and the different spreading characteristics of wavelet coefficients modular maximum between signal and noise in the different scales. Then the image denoising algorithm based on combining multi-scale product with wavelet threshold shrinkage is proposed in this paper. The experiments demonstrate that the algorithm proposed in the paper possesses preferable effect in image denoising.Subdivision scheme has become a focus of study in the world in computer aided geometric design and computer graphics recent years. After reviewing the general situation and history of subdivision, we introduce the elementary theories, the characteristic, the classification and application of subdivision. Through studying subdivision and subdivision wavelet, a new 4-point interpoloary subdivision wavelet is proposed in this paper. Then it is applied in image processing due to its muiltresolution analysis feature. The paper gave a primary discussion and research about the applications of subdivision scheme in image processing, which include image's segmentation, matching and repairing.Image segmentation is an important research content in image analysis and computer vision. It's task is segmenting the image into some areas according to one or lots of features of image. It is the key of image analysis and understanding and also the classic problem in image processing. Image segmentation is widely used in almost all fields of image processing. It involves all kinds of images. This paper summarizes and appraises usual algorithms of image segmentation through comparative study among different segmentation methods.Mean-Shift clustering algorithm is widely paid attention recently years. It is a nuclear density estimation and non-supervision clustering method. It possesses favorable capability in image segmentation. It's defects are excessive computational cost and long running time. An image segmentation algorithm is presented combining 4-point interpoloary subdivision wavelet proposed in this paper with Mean-Shift clustering method. It promotes the speed of image segmentation in the condition of keeping Mean-Shift algorithm's segmentation effect.Digital image match is the basic method in pattern recognition and image processing. It is widely used in satellitic remote sensing, automatic airmanship of space aerocraft, robot vision, meteorologic nephogram analysis and X-radial image processing in medicine. The primary problem to resolve in image match is promoting the speed in keeping some precision of image match. Classical template match algorithms have clear concept and it is simple to implement. But it has expensive computational cost so that it cann't satisfied real-time demand in image processing.According to the above questions, the image match algorithm based on local projection entropy and cubic B-spline subdivision wavelet was proposed in this paper. It promotes the speed of image match in some extent.This algorithm is applied to the tracking object of autonomous mobile robot and obtained good result.Image corruption and loss of data are always encountered in the processing of image compression, transmision and decompression, which had been paid attention by many researcheres who working on image processing. The loss of pixel groups when image encoding or incomplete restoration determined by decompression technique itself. For single pixel loss, general solution is to adopt the neighborhood point mean method and medium filter. When loss of pixel groups, the restoration is more difficult and sometimes it is impossible to restore the damaged image completely. Whereas, the relative information of appropriate fields around the pixel groups is chosen and some reasonable rule is established to restore the image .But complete restoration of the image is impossible. Using the advantage of subdivision in geometry modeling, the image repair algorithm based on 4-point interpoloary is presented in the paper. The subdivision come from modeling techniques of three dimensional free curve and surface in the domain of computer aided geometric design. Not only the groups of non-regular pixels can be repaired but also the repairing edges possess better smooth transition effect.Subdivision method has been widely applied to free curves and surfaces design for its simple representation and good approximating property among different layers. In the paper, subdivision has been introduced to the design of smooth principal curve and the good result has been obtained. First of all, in order to gain vertices of the initial control polygon the scattered data were initialized by Circle or Projection method. Then subdivide the initial control polygon some times and generate smooth principal curve. The comparison has been implemented between the principal curves generated from Circle-subdivision and Projection-subdivision apart. This offers reference for choosing the principal curve design method according to the target. The principal curve design method has the properties of simple representation, low cost and self-consistency.