#include <FDistanceAosPeronaOp.hpp>
Inheritance diagram for FDistanceAosPeronaOp:
Purpose: Perona with strong edge detection Fast Computation of Perona recursive flow. Thomas LU model. Semi-Implicit solver using AOS additive splitting (I - tau * A) * uNew= uOld Should be better than LOD: Rotation invariant. LOD is sequential (handelling each direction x y z in sequence) AOS is parallel (handelling each direction x y z at the same time) AOS build an average operator Both LOD and AOS are designed for large image restoration, O(N) in time ang space!!! Papers: "Recursivity and PDE's in image processing", L. Alvarez, R. Deriche and F Santana, Spain 1998. "Efficient and Reliable Schemes for Nonlinear Diffusion Filtering", Joachim. Weickert & all, IEEE transactions on Image Processing, vol7, n3, March 1998.
@ Copyrights: Bernard De Cuyper & Eddy Fraiha 2002, Eggs & Pictures. MIT/Open BSD copyright model.
Public Methods | |
FDistanceAosPeronaOp (FImage *edgeDistance=0, int iterations=8, float t0=0.0, float tMax=20.0, float deltaGradient=16.0, float asigma=5.0, bool iirFlag=false, bool nonLinearFlag=true) | |
virtual | ~FDistanceAosPeronaOp () |
virtual void | report (FILE *file) |
Protected Methods | |
virtual float | g (int i, int j) |