Image Denoising Using Mean Curvature of Image Surface

被引：143

作者：

Zhu, Wei ^{[1
]}

Chan, Tony ^{[2
]}

机构：

[1] Univ Alabama, Dept Math, Tuscaloosa, AL 35487 USA

[2] Hong Kong Univ Sci & Technol, Off President, Kowloon, Hong Kong, Peoples R China

来源：

SIAM JOURNAL ON IMAGING SCIENCES | 2012年 / 5卷 / 01期

关键词：

image denoising; mean curvature; variational model; TOTAL VARIATION MINIMIZATION; PARTIAL-DIFFERENTIAL-EQUATIONS; VARIATIONAL APPROACH; MODEL; SPACE;

D O I：

10.1137/110822268

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose a new variational model for image denoising, which employs the L-1-norm of the mean curvature of the image surface (x, f(x)) of a given image f : Omega -> R. Besides eliminating noise and preserving edges of objects efficiently, our model can keep corners of objects and greyscale intensity contrasts of images and also remove the staircase effect. In this paper, we analytically study the proposed model and justify why our model can preserve object corners and image contrasts. We apply the proposed model to the denoising of curves and plane images, and also compare the results with those obtained by using the classical Rudin-Osher-Fatemi model [Phys. D, 60 (1992), pp. 259-268].

引用

页码：1 / 32

页数：32

共 54 条

[21] ANALOGUE OF THE TOTAL VARIATION DENOISING MODEL IN THE CONTEXT OF GEOMETRY PROCESSING [J].