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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
+