Name
Abstract | ||
|---|---|---|
The neighborhood structure of a pixel in an image can be described more accurately by its two principal curvatures than its gradient or mean curvature-based estimation. Based on this idea, we propose a novel method - minimum principal curvature-driven diffusion, in which the two principal curvatures are used in a curvature-driven diffusion equation for image filtering. The main advantage of the proposed method over the existing methods is that it preserves not only conventional structures, such as edges, but also some fine structures such as ridges or thin lines. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICARCV.2008.4795502 | ICARCV |
Keywords | Field | DocType |
mean curvature-based estimation,principal curvature,curvature diffusion evolution,pixel neighborhood structure,minimum principal curvature-driven diffusion,image denoising,gradient estimation,partial differential equation (pde),denoising,image filtering,filtering theory,anisotropic diffusion,curvature-driven diffusion equation,noise measurement,diffusion equation,fine structure,noise,partial differential equation,mathematical model,psnr,mean curvature | Anisotropic diffusion,Noise reduction,Curvature,Noise measurement,Control theory,Computer science,Algorithm,Principal curvature,Filter (signal processing),Pixel,Geometry,Diffusion equation | Conference |
ISBN | Citations | PageRank |
978-1-4244-2287-6 | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong-nan Wang | 1 | 9 | 2.05 |
| Chun-xia Zhao | 2 | 9 | 1.04 |
| Haofeng Zhang | 3 | 78 | 10.36 |
| Yong Hu | 4 | 0 | 0.34 |
| Ming-ming Sun | 5 | 0 | 0.34 |