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# -*- coding: utf-8 -*-
#
# ANISODIFF - Anisotropic diffusion
#
# Usage:
# diff = anisodiff(img, niter, kappa, lambda, option)
#
# Arguments:
# | img - input image (2D grayscale)
# | niter - number of iterations
# | kappa - conduction coefficient (gradient modulus threshold)
# | This parameter controls conduction as a function of gradient.
# | If kappa is low, small intensity gradients are able to block
# | conduction and hence diffusion across step edges. A large value
# | reduces the influence of intensity gradients on conduction.
# | lambda - integration constant for stability (0 <= lambda <= 0.25)
# | This parameter controls the diffusion speed, and you
# | usually want it at the maximum value of 0.25.
# | default value: 0.25
# | option - conduction coefficient functions proposed by Perona & Malik:
# | 1: c(x, y, t) = exp(-(nablaI/kappa).^2)
# | privileges high-contrast edges over low-contrast ones
# | 2: c(x, y, t) = 1 ./ (1 + (nablaI/kappa).^2)
# | privileges wide regions over smaller ones
# | default value: 1
#
# Returns:
# | diff - anisotropic diffused image
#
# Reference:
# [1] P. Perona and J. Malik.
# Scale-space and edge detection using ansotropic diffusion.
# IEEE Transactions on Pattern Analysis and Machine Intelligence,
# 12(7):629-639, July 1990.
# https://dx.doi.org/10.1109%2F34.56205
#
# Credits:
# [1] Peter Kovesi
# pk@peterkovesi.com
# MATLAB and Octave Functions for Computer Vision and Image Processing
# http://www.peterkovesi.com/matlabfns/Spatial/anisodiff.m
# --
# June 2000 original version
# March 2002 corrected diffusion eqn No 2.
# [2] Daniel Lopes
# Anisotropic Diffusion (Perona & Malik)
# http://www.mathworks.com/matlabcentral/fileexchange/14995-anisotropic-diffusion--perona---malik-
#
#
# Aaron LI <aaronly.me@gmail.com>
# 2015/07/17
#
include("calc_k_percentile.jl");
function anisodiff(img, niter, k=calc_k_percentile, lambda=0.25, option=1)
diff = float(img)
rows, cols = size(diff)
for i = 1:niter
println("anisodiff - iteration: ", i)
# Construct diffl which is the same as diff but
# has an extra padding of zeros around it.
diffl = zeros(rows+2, cols+2)
diffl[2:rows+1, 2:cols+1] = diff
# North, South, East and West differences
deltaN = diffl[1:rows, 2:cols+1] - diff
deltaS = diffl[3:rows+2, 2:cols+1] - diff
deltaE = diffl[2:rows+1, 3:cols+2] - diff
deltaW = diffl[2:rows+1, 1:cols] - diff
# Calculate the kappa
if isa(k, Function)
kappa = k(diff)
else
kappa = k
end
println(" kappa: ", kappa)
# Conduction
if option == 1
cN = exp(-(deltaN/kappa).^2)
cS = exp(-(deltaS/kappa).^2)
cE = exp(-(deltaE/kappa).^2)
cW = exp(-(deltaW/kappa).^2)
elseif option == 2
cN = 1 ./ (1 + (deltaN/kappa).^2)
cS = 1 ./ (1 + (deltaS/kappa).^2)
cE = 1 ./ (1 + (deltaE/kappa).^2)
cW = 1 ./ (1 + (deltaW/kappa).^2)
end
diff += lambda * (cN.*deltaN + cS.*deltaS + cE.*deltaE + cW.*deltaW)
end
return diff
end
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