Image Processing Based On Partial Differential Equations

Over the past 20 years, with the development of computer technology, image processing get more and more attention and study. Image denoising is a key point in the fields of image processing. Image denoising can greatly improve image quality. In recent years, partial differential equation (PDE) methods in image processing have received extensive concern. Compared to other methods, partial differential equations have the following advantages: First, PDEs have a strong local self-adaptive (Local adaptability); Second, PDEs have the normative form (unification); Third, PDEs have a high degree of flexibility (flexibility) . Therefore, partial differential equations in the method of smoothing the noise at the same time will be able to better maintain the edge, texture, and other details.Partial differential equations (PDEs) have gone through the development of the theory from linear to nonlinear, as well as by the isotropic spread to anisotropic diffusion process. Anisotropic diffusion equation is a non-linear partial differential equations model. This article described the image denoising model of partial differential equations on the basis of a focus on non-linear structure of the tensor image edge enhancement models and equations of parameters were discussed. The results show that the non-linear structure of the tensor image edge enhancement model can effectively improve the quality of the image. Used of non-local concept of derivative,ROF model will be the promotion of local non-local anisotropic diffusion.