PDE For Human Ear Image Edge Preservation Smoothing Model

Ear recognition technology is emerging biometric technology. It would generate pseudo feature information for the noises of collector and different collection state produce displacement, rotation and deformation characteristics of the image on gathering ear image. Hence, before extracting the features of the human ear, we must filter noise, enhance image etc, the quality of smooth effect directly decides the ear recognition system effectiveness and accuracy. Traditional filter methods, such as Gaussian smoothing filter, mean filter and so on, inherently could not respect local features. The price paid for the removal of noise is the decrease of spatial resolution, which is caused by the flattening of sharp edges. In recent years, base on partial differential equations (Partial Differential Equations, PDEs) smoothing methods of image received widely attention in the field of digital image processing. And in many classic images processing issues, such as image filtering, restoration, segmentation, enhancement and other fields have achieved fruitful results.This paper is structured as follows:The first chapter is the introduction, an overview of the research topics of the purpose and meaning, introducing several conventional filtering methods, image processing evaluation criterion, later given the research work contents of this paper, and organizational structure.The second chapter is Partial Differential Equations in the development of image smoothing, and several typical PDE smoothing models are analyzed.The main attention is put on the chapter three, specifically the establishment of two improved nonlinear diffusion model for the human ear image processing, and their numerical solutions of the models, select the parameters for the use of Matlab simulation. Finally, the results summarized the advantages of the improved model.