Saddle-node dynamics for edge-preserving and scale-space filtering

Demonstrates the use of saddle-node dynamics for edge-preserving and scale-space filtering. The filter is derived from the maximum entropy principle and an analogy with statistical physics. The filter is governed by a single scale parameter which dictates the spatial extent of nearby data used for determining the output. This filter is thus highly adaptive and data-driven. It provides a mechanism for a) removing impulsive noise; b) improved smoothing of nonimpulsive noise and c) preserving edges. Comparisons with conventional techniques are made using real images.<>