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Study of Wavelet and Partial Differential Equations for Image Processing
Author: JiangDongHuan
Tutor: SongGuoXiang
School: Xi'an University of Electronic Science and Technology
Course: Applied Mathematics
Keywords: Wavelet transform Partial differential equations Wavelet shrinkage Scale space Edge detection Image decomposition Image zoom
CLC: TP391.41
Type: PhD thesis
Year: 2007
Downloads: 1779
Quote: 10
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Abstract
Wavelets are a new mathematical technique, arising in 1980’s. Wavelets have found wide applications in image processing due to its timefrequency localization and multiscale analysis property. It has been adopted as a key technique in the new still image compression standard JPEG2000. Recently, another active research is the application of Partial Differential Equations (PDEs) in image processing. In this dissertation, we focus on combining wavelets and PDEs for image processing. The main work can be summarized as follows:1. The relationship between wavelet shrinkage and scale space is discussed. On the one hand, wavelet shrinkage can be used to build a new image smoothing scale space; On the other hand, a new shrinkage function can be derived from the wavelet based scale space. A new translational invariant wavelet shrinkage function depending on Besov weight is further derived. Experiments show that the new shrinkage function is superior to the other wavelet shrinkages.2. As the PeronaMalik anisotropic diffusion cannot preserve edge details well, two improved diffusion models are proposed from the point of wavelet and morphology respectively, which are the anisotropic diffusion equation based on nonlinear wavelet shrinkage and on closeopen operator. The modified diffusion coefficient is dependent not only on the gradient of the image, but also on the second derivatives estimated on the image after being applied to nonlinear wavelet shrinkage or closeopen operator respectively. The most important advantage of the new models is that they can give a good tradeoff between noise removal and edge details preservation. Finally, numerical results are given.3. Two edge detectors are proposed. One is the Canny edge detection based on wavelet coupled shrinkage. As Gaussian filter results in oversmoothness and edge movement of the original image in the Canny edge detection, the new algorithm is proposed in which the Gaussian filter is replaced with the nonlinear wavelet coupled shrinkage. The other is the edge detection in the waveletbased scale space. From the scale space point of view, the multiscale version of the Canny edge detection is, in fact, the edge detection in a Gaussian scale space. The linear Gaussian scale space is substituted with a nonlinear waveletbased one, obtaining the edge detection in the waveletbased scale space. Experimental results demonstrate its superiority over the multiscale version of the Canny edge detection.4. Three algorithms of image decomposition are presented. Firstly, the image

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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