Digital Image Processing Based On Anisotropic Diffusion

With the informationization and digitalization over the society, digital image processing is becoming a more and more important subject. Partial differential equations based digital image processing received widely attention by academia because of its'strongly mathematical theoretical support. Anisotropic diffusion is a prevalent partial differential equations based digital image processing method. It developed from traditional Gaussian filter. The trait of anisotropic diffusion is to keeping characteristic of edge information of the image. Because of its good effects, anisotropic diffusion has been in-depth study and widely application.In this dissertation, we mainly study theory of anisotropic diffusion and the application of the technology and idea of anisotropic diffusion in image smooth, denoise, enhancement and segmentation. The main work includes:At first, we discuss the theory of anisotropic diffusion. The theory of Perona-Malik anisotropic diffusion are introduced and analyzed. Several important improved anisotropic diffusion methods to Perona-Malik anisotropic diffusion are led in. We make an assay of diffusion coefficient of anisotropic diffusion, and give a new principle to choose diffusion coefficient. The new choosing principle achieves better results in the experiment.After the foregoing discussion, we research the application of anisotropic diffusion in image smooth, denoise and enhancement. The Forward-and-Backward diffusion is introduced. It can smooth the image and enhance the edge feature. The Speckle Reduced Anisotropic Diffusion is recommended for remove speckle noise. A new denoising method to use anisotropic diffusion in stationary wavelet domain is raised. We introduce vision mask to anisotropic diffusion, and improve it. A new improved vision mask based anisotropic diffusion is raised and it can effectively remove noise in accordance with human vision.At last, this dissertation investigates how to use method and idea of anisotropic diffusion to image segmentation. The classic watershed for image segmentation is introduced and use Forward-and-Backward diffusion to reduce over-segmentation of watershed. The active contour model (Snake) and GVF snake model are introduced and we introduce how to improve GVF snake model using idea of anisotropic diffusion, thus a new method GGVF snake model is raised, it can be achieve better segmentation result.