
This paper aims to introduce a diffusion filtering based on the Laplacian map. Classical non-linear diffusion filtering using the gradient-map-controlled local diffusivity. The Laplacian maps has similar geometric properties with the gradient map for the extraction of the region boundaries. Laplacian-based diffusion function has the same property with the Perona-Malik type and the Weickert type diffusion functions for the small scale. However, for the large scale, the diffusion operation has similar geometrical properties with the linear diffusion filtering. Therefore, the filtering operation in this paper provides a method for the combination of hierarchical expression based on linear and non-linear diffusion filtering operations.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
