Application Of Partial Differential Equation In Image Denoising

Images are important sources of information for human beings,the quality of images is related to how much information can be received.The ultimate goal of denoising is to improve the given image and solve the problem that the image quality is degraded due to noise interference.Through denoising technology can effectively improve the image quality,increase the signal-to-noise ratio,better reflect the original image of the information carried,as an important pretreatment means,people to the image denoising algorithm has been widely studied.In the existing denoising algorithm,some denoising algorithms can achieve better results in low dimensional signal image processing,but they do not apply to high dimensional signal image processing,or the denoising effect is good,but some image edge information is lost,or the image edge information is studied,and the details are preserved.How to find a better balance in resisting noise and keeping details has become the focus of research in recent years.The image denoising process is to reduce the noise in the image,as well as improve the image quality.The image denoising algorithms require removing the noise while preserving the edge details of the image as much as possible.The partial differential image denoising algorithm studied in this paper is an emerging cross-disciplinary branch which is superior to the traditional image denoising algorithm in the theoretical analysis and denoising effect,and can effectively maintain the balance between removing noise and preserving the edge detail structure,as well as provide theoretical support for constructing new denoising models.This article introduces the basic knowledge of image denoising in detail,such as basic concepts,basic theories and basic algorithms.The expressions of partial differential equations are studied and analyzed.Two classic model---PM models and TV models are deduced,and the numerical experimental results are given.