Noise removal with Gauss curvature-driven diffusion

Authors:
Suk-Ho Lee;Jin Keun Seo
Affiliations:
Dept. of Math., Yonsei Univ., Seoul, South Korea;-
Venue:
IEEE Transactions on Image Processing
Year:
2005

Citing 0
Cited 8

Bayesian algorithms for PET image reconstruction with mean curvature and Gauss curvature diffusion regularizations

Computers in Biology and Medicine
Adaptive total variation denoising based on difference curvature

Image and Vision Computing
Ramp preserving Perona-Malik model

Signal Processing
The variational origin of motion by Gaussian curvature

SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Homogeneity similarity based image denoising

Pattern Recognition
A Multilevel Algorithm for Simultaneously Denoising and Deblurring Images

SIAM Journal on Scientific Computing
Geometry of total variation regularized Lp-model

Journal of Computational and Applied Mathematics
An effective and objective criterion for evaluating the performance of denoising filters

Pattern Recognition

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we propose the use of the Gauss curvature in a Gauss curvature-driven diffusion equation for noise removal. The proposed scheme uses the Gauss curvature as the conductance term and controls the amount of diffusion. The main advantage of the scheme is that it preserves important structures, such as straight edges, curvy edges, ramps, corners, small-scaled features, etc.