#include <RAosPeronaFabOp.hpp>
Inheritance diagram for RAosPeronaFabOp:
Purpose: FAB diffusion may be unstable if edges are too strong.
It allow to blurr/deblurr adaptively. We reduce deblurring inversely to number of iterations.
We replace the old Perona barrier by the combined forward/backward. The process can be instable.
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: "Forward and Backward Diffusion Processes for Adaptive Image Enhancement and Denoising.",
Guy Gilboa, Nir Sochen and Yehoshua Y. Zeevi, IEEE Trans. on Image Processing, vol ?, no ? 2002
"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 | |
| RAosPeronaFabOp (int iterations=8, double t=0.5, int nf=4, int nb=2, double gSigma=3.0, double rkf=0.1, double asigma=5.0, double rkb=6.0, double rw=2.0, bool iirFlag=false, bool nonLinearFlag=true) | |
| virtual | ~RAosPeronaFabOp () |
| virtual RImage * | filter (RImage *src, RImage *dest=0) |
| Local full image filtering. | |
| virtual void | report (FILE *file) |
Protected Methods | |
| virtual double | g (int i, int j) |
Protected Attributes | |
| RImage * | I0 |
| double | gsigma |
| RImage * | gradI0 |
| int | nf |
| double | kf |
| int | nb |
| double | alpha |
| double | kb |
| double | wb |
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Local full image filtering.
Reimplemented from RSimpleAosOp. |
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