do_andiff_filtering_backup.m File Reference

Filter dataset with anisotropic diffusion. More...

Functions
function [ img , status ] =	do_andiff_filtering_backup (img, options)
	Filter dataset with anisotropic diffusion. More...

Detailed Description

Filter dataset with anisotropic diffusion.

Function Documentation

function [ img , status ] = do_andiff_filtering_backup	(		img,
			options
	)

Filter dataset with anisotropic diffusion.

Use filters from the Diplib library (http://www.diplib.org) or anisodiff function by Peter Kovesi (http://www.csse.uwa.edu.au/~pk/Research/MatlabFns/#anisodiff)

Parameters

img

-> input image img{roi}(1:height, 1:width, 1:color, 1:layers)

options

-> structure with parameters .Filter = 'anisodiff', Perona Malik anisotropic diffusion .Filter = 'pmd', Perona Malik anisotropic diffusion, diplib .Filter = 'aniso', Robust Anisotropic Diffusion using Tukey error norm, diplib .Filter = 'mcd', Mean Curvature Diffusion, diplib .Filter = 'cpf', Nonlinear Diffusion using Corner Preserving Formula (improved over MCD), diplib .Filter = 'kuwahara', Kuwahara filter for edge-preserving smoothing, diplib .Iter -> number of iterations, or shape of the kuwahara filter (0-rectangular, 1-elliptic, 2-diamond) .KSigma -> K, edge stopping parameter (pmd), or Sigma, .Lambda -> rate parameter (pmd, aniso) .Favours -> 1: favours high contrast edges over low contrast ones; 2: favours wide regions over smaller ones. For anisodiff only .Orientation -> orientation parameter: 4 - for xy, 1 - for xz, 2 - for yz .start_no -> first index .end_no -> start index .Color -> color channel, when 0, do for all colors

Return values

img	-> output image
status	-> 1 - success, 0 - fail

Required fields of options:

References anisodiff(), and roiRegion.roi.

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