Modified mean curvature motion for multispectral anisotropic diffusion
This paper introduces a new anisotropic diffusion algorithm for enhancing and segmenting multispectral image data. The algorithm is based upon mean curvature motion. Using a modified image gradient computation, the diffusion method is further improved by allowing the control of feature scale, and the sensitivity to heavy-tailed noise is eliminated. For comparison, a vector distance dissimilarity method is introduced and extended for multi-scale processing. The experiments on remotely sensed imagery and color imagery demonstrate the performance of the algorithms in terms of image entropy reduction and impulse elimination as well as visual quality.