Curvature Diffusion Evolution in Image Filtering

摘要

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.