Research on the Application of Mathematical Methods in Image Denoising Technology

Abstract

In the process of acquisition, compression, transmission and storage, digital images are often interfered by imaging equipment and the external environment, resulting in image distortion. Image denoising aims to truly reconstruct the image from the observation data contaminated by noise to provide more accurate and reliable information for the subsequent processing of the image. Therefore, image noise reduction is the most basic and most important link in the field of image processing research. In the past decade or so, the application of theories and methods of partial differential equations (PDEs) in various fields of image processing has attracted more and more attention. This article researches and analyzes the image processing methods based on partial differential equations. First, it introduces the research background of this article, the development history, principles of partial differential equations and the status quo of image processing; then, it summarizes and analyzes the application of partial differential equations to images Classical model of denoising. Through the analysis of the classic model, it can be known that the image noise reduction algorithm based on partial differential equations can be based on the structural characteristics of the image. This method adopts a corresponding smoothing strategy, and it can achieve satisfactory image denoising effects.