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diff --git a/octave_packages/image-1.0.15/entropyfilt.m b/octave_packages/image-1.0.15/entropyfilt.m
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+## Copyright (C) 2008 Søren Hauberg
+## 
+## This program is free software; you can redistribute it and/or modify
+## it under the terms of the GNU General Public License as published by
+## the Free Software Foundation; either version 3 of the License, or
+## (at your option) any later version.
+## 
+## This program is distributed in the hope that it will be useful,
+## but WITHOUT ANY WARRANTY; without even the implied warranty of
+## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+## GNU General Public License for more details.
+## 
+## You should have received a copy of the GNU General Public License
+## along with this program; if not, write to the Free Software
+## Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
+
+## -*- texinfo -*-
+## @deftypefn {Function File} {@var{E} =} entropyfilt (@var{im})
+## @deftypefnx{Function File} {@var{E} =} entropyfilt (@var{im}, @var{domain})
+## @deftypefnx{Function File} {@var{E} =} entropyfilt (@var{im}, @var{domain}, @var{padding}, ...)
+## Computes the local entropy in a neighbourhood around each pixel in an image.
+##
+## The entropy of the elements of the neighbourhood is computed as
+##
+## @example
+## @var{E} = -sum (@var{P} .* log2 (@var{P})
+## @end example
+##
+## where @var{P} is the distribution of the elements of @var{im}. The distribution
+## is approximated using a histogram with @var{nbins} cells. If @var{im} is
+## @code{logical} then two cells are used. For other classes 256 cells
+## are used.
+##
+## When the entropy is computed, zero-valued cells of the histogram are ignored.
+##
+## The neighbourhood is defined by the @var{domain} binary mask. Elements of the
+## mask with a non-zero value are considered part of the neighbourhood. By default
+## a 9 by 9 matrix containing only non-zero values is used.
+##
+## At the border of the image, extrapolation is used. By default symmetric
+## extrapolation is used, but any method supported by the @code{padarray} function
+## can be used. Since extrapolation is used, one can expect a lower entropy near
+## the image border.
+##
+## @seealso{entropy, paddarray, stdfilt}
+## @end deftypefn
+
+function retval = entropyfilt (I, domain = true (9), padding = "symmetric", varargin)
+  ## Check input
+  if (nargin == 0)
+    error ("entropyfilt: not enough input arguments");
+  endif
+  
+  if (!ismatrix (I))
+    error ("entropyfilt: first input must be a matrix");
+  endif
+  
+  if (!ismatrix (domain))
+    error ("entropyfilt: second input argument must be a logical matrix");
+  endif
+  domain = (domain > 0);
+  
+  ## Get number of histogram bins
+  if (islogical (I))
+    nbins = 2;
+  else
+    nbins = 256;
+  endif
+  
+  ## Convert to 8 or 16 bit integers if needed
+  switch (class (I))
+    case {"double", "single", "int16", "int32", "int64", "uint16", "uint32", "uint64"}
+      min_val = double (min (I (:)));
+      max_val = double (max (I (:)));
+      if (min_val == max_val)
+        retval = zeros (size (I));
+        return;
+      endif
+      I = (double (I) - min_val)./(max_val - min_val);
+      I = uint8 (255 * I);
+    case {"logical", "int8", "uint8"}
+      ## Do nothing
+    otherwise
+      error ("entropyfilt: cannot handle images of class '%s'", class (I));
+  endswitch
+  size (I)
+  ## Pad image
+  pad = floor (size (domain)/2);
+  I = padarray (I, pad, padding, varargin {:});
+  even = (round (size (domain)/2) == size (domain)/2);
+  idx = cell (1, ndims (I));
+  for k = 1:ndims (I)
+    idx {k} = (even (k)+1):size (I, k);
+  endfor
+  I = I (idx {:});
+  size (I)
+  ## Perform filtering
+  retval = __spatial_filtering__ (I, domain, "entropy", I, nbins);
+
+endfunction