#include <CAosPeronaOp.hpp>
Inheritance diagram for CAosPeronaOp:
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: "Complex Diffusion Processes in Image Filtering",
Guy Gilboa & all, Report 2001, Israel Institut of Technology.
"Image Enhancement and Denoising by Complex Diffusion Processes",
Guy Gilboa, Zeevi, Sochen, Report August 2002, Israel Institut of Technology.
"Evaluation of Diffusion Schemes for Multi-scale Watershed Segmentation",
Erik Dam, 2000.
"Recursivity and PDE's in image processing",
L. Alvarez, R. Deriche and F Santana, Spain 1998.
"Efficient and Reliable Schemes for Nonlinear Diffusion Filtering",
J. Weickert & all, IEEE transact. on image processing, vol7, n3, March 1998.
@ Copyrights: Bernard De Cuyper & Eddy Fraiha 2003, Eggs & Pictures. MIT/Open BSD copyright model.
Public Methods | |
| CAosPeronaOp (int iterations=8, double t=0.5, double ctheta=0.001, double deltaGradient=16.0, double asigma=5.0, bool iirFlag=false, bool nonLinearFlag=true) | |
| virtual | ~CAosPeronaOp () |
| virtual void | report (FILE *file) |
Protected Methods | |
| virtual RComplex | g (int i, int j) |
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