https://svn/svn/oln/prototypes/proto-1.0/olena Index: ChangeLog from Nicolas Widynski &lt;nicolas.widynski(a)lrde.epita.fr&gt; Implementation of a denoising image processing algorithm : Non Local Means. * oln/appli/denoising/nl_means.hh: New. NL-means algorithm. * oln/makefile.src: Update for laplacian convolution and NL-means algorithm. appli/denoising/nl_means.hh | 141 ++++++++++++++++++++++++++++++++++++++++++++ makefile.src | 4 + 2 files changed, 145 insertions(+) Index: oln/appli/denoising/nl_means.hh --- oln/appli/denoising/nl_means.hh (revision 0) +++ oln/appli/denoising/nl_means.hh (revision 0) @@ -0,0 +1,141 @@ +// Copyright (C) 2001, 2002, 2004, 2005, 2006 EPITA Research and Development Laboratory +// +// This file is part of the Olena Library. This library is free +// software; you can redistribute it and/or modify it under the terms +// of the GNU General Public License version 2 as published by the +// Free Software Foundation. +// +// This library 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 library; see the file COPYING. If not, write to +// the Free Software Foundation, 59 Temple Place - Suite 330, Boston, +// MA 02111-1307, USA. +// +// As a special exception, you may use this file as part of a free +// software library without restriction. Specifically, if other files +// instantiate templates or use macros or inline functions from this +// file, or you compile this file and link it with other files to +// produce an executable, this file does not by itself cause the +// resulting executable to be covered by the GNU General Public +// License. This exception does not however invalidate any other +// reasons why the executable file might be covered by the GNU General +// Public License. + +#ifndef OLENA_DENOISING_NLMEANS_HH +# define OLENA_DENOISING_NLMEANS_HH + +# define SQR(u) u*u +# define PI 3.141592653589793238462643 + +# include <cmath> +# include <oln/level/fill.hh> + +namespace oln { + + namespace denoising { + + namespace impl { + + + template<typename I> + const oln_type_of(I, concrete) gaussian_mask(double variance, + int rows, + int cols) + { + oln_type_of(I, concrete) g_m(rows, cols, 0); + + int i, j; + + for (i = 0; i < rows; i++) + for (j = 0; j < cols; j++) + g_m(i,j) = (1 / 2 * PI * SQR(variance)) * exp(-(SQR(rows / 2 - i) + SQR(cols / 2 - j))); + + return g_m; + } + + + template<typename I> + oln_type_of(I, concrete) nl_means_(const oln::abstract::image<I>& input, + int window, + double variance, + int horizon) + { + oln_type_of(I, concrete) nl(input.size(), "nl_means"); + oln_type_of(I, concrete) g_m; + oln_type_of(I, fwd_piter) p(input.size()); + oln_type_of(I, point) pt1; + oln_type_of(I, point) pt2; + + int i, j, k, l, i2, j2, k2, l2; + double dist; + double w; + double Z; + double val; + + + level::fill(nl, 0); + + g_m = gaussian_mask<I>(variance, window, window); + + for (i = 0; i < input.size().ncols(); i++) + { + for (j = 0; j < input.size().nrows(); j++) + { + Z = 0; + val = 0; + + for (k = i - horizon; k < i + horizon; k++) + for (l = j - horizon; l < j + horizon; l++) + { + if (not (k >= 0 and k < (int)input.size().ncols() and l >= 0 and l < (int)input.size().nrows())) + continue; + + dist = 0; + w = 0; + + for (k2 = k - window / 2, i2 = i - window / 2; k2 < (k + window / 2 + window % 2); k2++, i2++) + for (l2 = l - window / 2, j2 = j - window / 2; l2 < (l + window / 2 + window % 2); l2++, j2++) + + if (i2 >= 0 and i2 < input.size().ncols() && j2 >= 0 and j2 < input.size().nrows() and + k2 >= 0 and k2 < input.size().ncols() && l2 >= 0 and l2 < input.size().nrows()) + { + dist += g_m.exact()(k2 + window / 2 - k, l2 + window / 2 - l) * + ((input.exact()(i2, j2) - input.exact()(k2, l2)) * + (input.exact()(i2, j2) - input.exact()(k2, l2))); + } + + w = exp(-dist/(SQR(variance))); + val += w * input.exact()(k, l).value(); + Z += w; + } + + nl(i,j) = val / Z; + } + + std::cout << (int)input.size().nrows() << " / " << i + 1 << std::endl; + } + + return nl; + } + + } // end of namespace oln::denoising::impl + + template<typename I> + oln_type_of(I, concrete) nl_means(const oln::abstract::image<I>& input, + int window, + double variance, + int horizon) + { + return impl::nl_means_(input.exact(), window, variance, horizon); + } + + + } // end of namespace oln::denoising + +} // end of namespace oln + +#endif // OLENA_DENOISING_NLMEANS_HH Index: oln/makefile.src --- oln/makefile.src (revision 390) +++ oln/makefile.src (working copy) @@ -10,6 +10,8 @@ appli/astro/tree_coherence_check.hh \ appli/astro/tree_statistics.hh \ \ + appli/denoising/nl_means.hh \ + \ basics1d.hh \ basics2d.hh \ basics3d.hh \ @@ -30,6 +32,8 @@ convert/stretch.hh \ convert/value_to_point.hh \ \ + convolution/laplacian.hh \ + \ core/1d/array1d.hh \ core/1d/bkd_piter1d.hh \ core/1d/dpoint1d.hh \