Research On Image Denoising Method Based On Partial Differential Equation

Image restoration is one of the most important research fields of image processing, the image processing and image denoising is a hot issue. Images in the process of acquisition, transmission and storage, due to some equipment or incomplete certain physical limitations, the image will be infected with some noise, image quality will be greatly reduced.This image visual effect will be affected by a lot, but also for subsequent processing of the image brought some unnecessary trouble, therefore, image denoising is an important content in the research of image processing.This article mainly to the denoising model of partial differential equation method as the core, this paper introduces the traditional denoising model and improved the variational denoising model. The first chapter mainly introduces the research background and significance of image filtering, this paper expounds the variational method development and research status of image denoising, and summary of the content is given in this paper. The second chapter tells the story of the basic model of image noise and the traditional image denoising methods. Third chapter mainly summarizes the traditional PDE image denoising of several models, and analyzed several typical noise filter method. Third chapter mainly summarizes the traditional PDE image denoising of several models, and analyzed several typical noise filter method. The fourth chapter and the fifth chapter mainly tells the story of two kinds of improved model based on variational method. The fourth chapter on the basis of the total variation model, Stokes denoising model is put forward, the first step in this model is mainly through the establishment of the energy functional on the tangent vector, the second step is through the establishment of minimization of energy functional, looking for original image to match the cutting direction, coupled with difference aspects of mathematics knowledge, the satisfactory results obtained. In the fifth chapter of the article puts forward a method of two step recovery, to recover the blurred image damaged by impulse noise. Chapter v of the denoising process of this algorithm is divided into two stages:noise testing phase and recovery phase noise. Noise testing process, the sliding window, expand the current pixel values and other orderly differences between pixel values, to determine whether the current pixel noise pixels. Noise in the recovery process using the variational method, it can protect the image edges and details. Experimental results show that the proposed detection method and noise reduction method in the case of high noise density is superior to other algorithms.