Face Image Denoising Method Based on Fourth-Order Partial Differential Equations

Weng Cang Zhao,Fan Wang

doi:10.4028/www.scientific.net/aef.6-7.700

Abstract

In order to improve the effect of face image denoising, this paper put forward several face image denoising methods based on partial differential equations, including P-M non-linear diffusion equations and fourth-order partial differential equations. We use those two methods by establishing non-linear diffusion equations and fourth-order anisotropic diffusion partial differential equation. The P-M non-linear diffusion denoising method can remove noise in intra-regions sufficiently but noise at edges can not be eliminated successfully and line-like structures can not be held very well.While the fourth-order partial differential equations denoising can retain the local detail characteristics of the original face image. Finally, through the experimental results we can see the effect of the fourth-order partial differential equations denoising is better, which makes the later face image processing more accurate and promotes the development of face image processing.

Highlights

Face recognition is one of the controversial research projects in recent years, it's relevant technologies play a very important role on the development of identity recognition, city security and other fields[1]
Conduction coefficient can automatically increase at the relatively flat areas of image, which can ensure that the smaller irregular ups and downs in flat area is smooth.And near the edges of the image,conduction coefficient can be reduced automatically, so the image edges can be hardly affected [3]
In order to compensate for its lack of face image denoising, this paper proposes a method of the fourth-order partial differential equations applied to the face image denoising

Summary

Introduction

Face recognition is one of the controversial research projects in recent years, it's relevant technologies play a very important role on the development of identity recognition, city security and other fields[1]. This paper analyzes the traditional P-M non-linear diffusion method.The P-M equation is put forward by Perona and Malik in 1990[2]. G ∇ut is called diffusion ( ) function whose value indicates diffusion intensity, and usually g ∇ut is a non-negative smooth monotone decreasing function This method in image denoising can avoid deficiencies well about that thermal diffusion can not select denoising on image edges and smooth regions. If there is excessive denoising on image, it will lead to image and gray image into a subdivision, namely the staircasing effect It is inconvenience for follow-up face image processing. The P-M equation processed image will cause the staircasing effect, so in order to solve this problem, You Yu-Li and M.Kavch [5] put forward the following fourth-order anisotropic diffusion partial differential equation in 2000.

Information Technology for Manufacturing Systems III

Summary