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    00001 /************************************************************************/
    00002 /*                                                                      */
    00003 /*               Copyright 1998-2002 by Ullrich Koethe                  */
    00004 /*       Cognitive Systems Group, University of Hamburg, Germany        */
    00005 /*                                                                      */
    00006 /*    This file is part of the VIGRA computer vision library.           */
    00007 /*    ( Version 1.5.0, Dec 07 2006 )                                    */
    00008 /*    The VIGRA Website is                                              */
    00009 /*        http://kogs-www.informatik.uni-hamburg.de/~koethe/vigra/      */
    00010 /*    Please direct questions, bug reports, and contributions to        */
    00011 /*        koethe@informatik.uni-hamburg.de          or                  */
    00012 /*        vigra@kogs1.informatik.uni-hamburg.de                         */
    00013 /*                                                                      */
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    00015 /*    obtaining a copy of this software and associated documentation    */
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    00021 /*    conditions:                                                       */
    00022 /*                                                                      */
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    00024 /*    included in all copies or substantial portions of the             */
    00025 /*    Software.                                                         */
    00026 /*                                                                      */
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    00035 /*                                                                      */
    00036 /************************************************************************/
    00037
    00038 #ifndef VIGRA_NONLINEARDIFFUSION_HXX
    00039 #define VIGRA_NONLINEARDIFFUSION_HXX
    00040
    00041 #include <vector>
    00042 #include "stdimage.hxx"
    00043 #include "stdimagefunctions.hxx"
    00044 #include "imageiteratoradapter.hxx"
    00045 #include "functortraits.hxx"
    00046
    00047 namespace vigra {
    00048
    00049 template <class SrcIterator, class SrcAccessor,
    00050           class CoeffIterator, class DestIterator>
    00051 void internalNonlinearDiffusionDiagonalSolver(
    00052     SrcIterator sbegin, SrcIterator send, SrcAccessor sa,
    00053     CoeffIterator diag, CoeffIterator upper, CoeffIterator lower,
    00054     DestIterator dbegin)
    00055 {
    00056     int w = send - sbegin - 1;
    00057
    00058     int i;
    00059
    00060     for(i=0; i<w; ++i)
    00061     {
    00062         lower[i] = lower[i] / diag[i];
    00063
    00064         diag[i+1] = diag[i+1] - lower[i] * upper[i];
    00065     }
    00066
    00067     dbegin[0] = sa(sbegin);
    00068
    00069     for(i=1; i<=w; ++i)
    00070     {
    00071         dbegin[i] = sa(sbegin, i) - lower[i-1] * dbegin[i-1];
    00072     }
    00073
    00074     dbegin[w] = dbegin[w] / diag[w];
    00075
    00076     for(i=w-1; i>=0; --i)
    00077     {
    00078         dbegin[i] = (dbegin[i] - upper[i] * dbegin[i+1]) / diag[i];
    00079     }
    00080 }
    00081
    00082
    00083 template <class SrcIterator, class SrcAccessor,
    00084           class WeightIterator, class WeightAccessor,
    00085           class DestIterator, class DestAccessor>
    00086 void internalNonlinearDiffusionAOSStep(
    00087                    SrcIterator sul, SrcIterator slr, SrcAccessor as,
    00088                    WeightIterator wul, WeightAccessor aw,
    00089                    DestIterator dul, DestAccessor ad, double timestep)
    00090 {
    00091     // use traits to determine SumType as to prevent possible overflow
    00092     typedef typename
    00093         NumericTraits<typename DestAccessor::value_type>::RealPromote
    00094         DestType;
    00095
    00096     typedef typename
    00097         NumericTraits<typename WeightAccessor::value_type>::RealPromote
    00098         WeightType;
    00099
    00100     // calculate width and height of the image
    00101     int w = slr.x - sul.x;
    00102     int h = slr.y - sul.y;
    00103     int d = (w < h) ? h : w;
    00104
    00105     std::vector<WeightType> lower(d),
    00106                             diag(d),
    00107                             upper(d);
    00108
    00109     std::vector<DestType> res(d);
    00110
    00111     int x,y;
    00112
    00113     WeightType one = NumericTraits<WeightType>::one();
    00114
    00115      // create y iterators
    00116     SrcIterator ys = sul;
    00117     WeightIterator yw = wul;
    00118     DestIterator yd = dul;
    00119
    00120     // x-direction
    00121     for(y=0; y<h; ++y, ++ys.y, ++yd.y, ++yw.y)
    00122     {
    00123         typename SrcIterator::row_iterator xs = ys.rowIterator();
    00124         typename WeightIterator::row_iterator xw = yw.rowIterator();
    00125         typename DestIterator::row_iterator xd = yd.rowIterator();
    00126
    00127         // fill 3-diag matrix
    00128
    00129         diag[0] = one + timestep * (aw(xw) + aw(xw, 1));
    00130         for(x=1; x<w-1; ++x)
    00131         {
    00132             diag[x] = one + timestep * (2.0 * aw(xw, x) + aw(xw, x+1) + aw(xw, x-1));
    00133         }
    00134         diag[w-1] = one + timestep * (aw(xw, w-1) + aw(xw, w-2));
    00135
    00136         for(x=0; x<w-1; ++x)
    00137         {
    00138             lower[x] = -timestep * (aw(xw, x) + aw(xw, x+1));
    00139             upper[x] = lower[x];
    00140         }
    00141
    00142         internalNonlinearDiffusionDiagonalSolver(xs, xs+w, as,
    00143                             diag.begin(), upper.begin(), lower.begin(), res.begin());
    00144
    00145         for(x=0; x<w; ++x, ++xd)
    00146         {
    00147             ad.set(res[x], xd);
    00148         }
    00149     }
    00150
    00151     // y-direction
    00152     ys = sul;
    00153     yw = wul;
    00154     yd = dul;
    00155
    00156     for(x=0; x<w; ++x, ++ys.x, ++yd.x, ++yw.x)
    00157     {
    00158         typename SrcIterator::column_iterator xs = ys.columnIterator();
    00159         typename WeightIterator::column_iterator xw = yw.columnIterator();
    00160         typename DestIterator::column_iterator xd = yd.columnIterator();
    00161
    00162         // fill 3-diag matrix
    00163
    00164         diag[0] = one + timestep * (aw(xw) + aw(xw, 1));
    00165         for(y=1; y<h-1; ++y)
    00166         {
    00167             diag[y] = one + timestep * (2.0 * aw(xw, y) + aw(xw, y+1) + aw(xw, y-1));
    00168         }
    00169         diag[h-1] = one + timestep * (aw(xw, h-1) + aw(xw, h-2));
    00170
    00171         for(y=0; y<h-1; ++y)
    00172         {
    00173             lower[y] = -timestep * (aw(xw, y) + aw(xw, y+1));
    00174             upper[y] = lower[y];
    00175         }
    00176
    00177         internalNonlinearDiffusionDiagonalSolver(xs, xs+h, as,
    00178                             diag.begin(), upper.begin(), lower.begin(), res.begin());
    00179
    00180         for(y=0; y<h; ++y, ++xd)
    00181         {
    00182             ad.set(0.5 * (ad(xd) + res[y]), xd);
    00183         }
    00184     }
    00185 }
    00186
    00187 /** \addtogroup NonLinearDiffusion Non-linear Diffusion
    00188
    00189     Perform edge-preserving smoothing.
    00190 */
    00191 //@{
    00192
    00193 /********************************************************/
    00194 /*                                                      */
    00195 /*                  nonlinearDiffusion                  */
    00196 /*                                                      */
    00197 /********************************************************/
    00198
    00199 /** \brief Perform edge-preserving smoothing at the given scale.
    00200
    00201     The algorithm solves the non-linear diffusion equation
    00202
    00203     \f[
    00204         \frac{\partial}{\partial t} u =
    00205         \frac{\partial}{\partial x}
    00206           \left( g(|\nabla u|)
    00207                  \frac{\partial}{\partial x} u
    00208           \right)
    00209     \f]
    00210
    00211     where <em> t</em> is the time, <b> x</b> is the location vector,
    00212     <em> u(</em><b> x</b><em> , t)</em> is the smoothed image at time <em> t</em>, and
    00213     <em> g(.)</em> is the location dependent diffusivity. At time zero, the image
    00214     <em> u(</em><b> x</b><em> , 0)</em> is simply the original image. The time is
    00215     propotional to the square of the scale parameter: \f$t = s^2\f$.
    00216     The diffusion
    00217     equation is solved iteratively according
    00218     to the Additive Operator Splitting Scheme (AOS) from
    00219
    00220
    00221     J. Weickert: <em>"Recursive Separable Schemes for Nonlinear Diffusion
    00222     Filters"</em>,
    00223     in: B. ter Haar Romeny, L. Florack, J. Koenderingk, M. Viergever (eds.):
    00224         1st Intl. Conf. on Scale-Space Theory in Computer Vision 1997,
    00225         Springer LNCS 1252
    00226
    00227     <TT>DiffusivityFunctor</TT> implements the gradient dependent local diffusivity.
    00228     It is passed
    00229     as an argument to \ref gradientBasedTransform(). The return value must be
    00230     between 0 and 1 and determines the weight a pixel gets when
    00231     its neighbors are smoothed. Weickert recommends the use of the diffusivity
    00232     implemented by class \ref DiffusivityFunctor. It's also possible to use
    00233     other functors, for example one that always returns 1, in which case
    00234     we obtain the solution to the linear diffusion equation, i.e.
    00235     Gaussian convolution.
    00236
    00237     The source value type must be a
    00238     linear space with internal addition, scalar multiplication, and
    00239     NumericTraits defined. The value_type of the DiffusivityFunctor must be the
    00240     scalar field over wich the source value type's linear space is defined.
    00241
    00242     In addition to <TT>nonlinearDiffusion()</TT>, there is an algorithm
    00243     <TT>nonlinearDiffusionExplicit()</TT> which implements the Explicit Scheme
    00244     described in the above article. Both algorithms have the same interface,
    00245     but the explicit scheme gives slightly more accurate approximations of
    00246     the diffusion process at the cost of much slower processing.
    00247
    00248     <b> Declarations:</b>
    00249
    00250     pass arguments explicitly:
    00251     \code
    00252     namespace vigra {
    00253         template <class SrcIterator, class SrcAccessor,
    00254                   class DestIterator, class DestAccessor,
    00255                   class DiffusivityFunctor>
    00256         void nonlinearDiffusion(SrcIterator sul, SrcIterator slr, SrcAccessor as,
    00257                            DestIterator dul, DestAccessor ad,
    00258                            DiffusivityFunctor const & weight, double scale);
    00259     }
    00260     \endcode
    00261
    00262
    00263     use argument objects in conjunction with \ref ArgumentObjectFactories:
    00264     \code
    00265     namespace vigra {
    00266         template <class SrcIterator, class SrcAccessor,
    00267                   class DestIterator, class DestAccessor,
    00268                   class DiffusivityFunctor>
    00269         void nonlinearDiffusion(
    00270             triple<SrcIterator, SrcIterator, SrcAccessor> src,
    00271             pair<DestIterator, DestAccessor> dest,
    00272             DiffusivityFunctor const & weight, double scale);
    00273     }
    00274     \endcode
    00275
    00276     <b> Usage:</b>
    00277
    00278     <b>\#include</b> "<a href="nonlineardiffusion_8hxx-source.html">vigra/nonlineardiffusion.hxx</a>"
    00279
    00280
    00281     \code
    00282     FImage src(w,h), dest(w,h);
    00283     float edge_threshold, scale;
    00284     ...
    00285
    00286     nonlinearDiffusion(srcImageRange(src), destImage(dest),
    00287                        DiffusivityFunctor<float>(edge_threshold), scale);
    00288     \endcode
    00289
    00290     <b> Required Interface:</b>
    00291
    00292     <ul>
    00293
    00294     <li> <TT>SrcIterator</TT> and <TT>DestIterator</TT> are models of ImageIterator
    00295     <li> <TT>SrcAccessor</TT> and <TT>DestAccessor</TT> are models of StandardAccessor
    00296     <li> <TT>SrcAccessor::value_type</TT> is a linear space
    00297     <li> <TT>DiffusivityFunctor</TT> conforms to the requirements of
    00298           \ref gradientBasedTransform(). Its range is between 0 and 1.
    00299     <li> <TT>DiffusivityFunctor::value_type</TT> is an algebraic field
    00300
    00301     </ul>
    00302
    00303     <b> Precondition:</b>
    00304
    00305     <TT>scale > 0</TT>
    00306 */
    00307 template <class SrcIterator, class SrcAccessor,
    00308           class DestIterator, class DestAccessor,
    00309           class DiffusivityFunc>
    00310 void nonlinearDiffusion(SrcIterator sul, SrcIterator slr, SrcAccessor as,
    00311                    DestIterator dul, DestAccessor ad,
    00312                    DiffusivityFunc const & weight, double scale)
    00313 {
    00314     vigra_precondition(scale > 0.0, "nonlinearDiffusion(): scale must be > 0");
    00315
    00316     double total_time = scale*scale/2.0;
    00317     static const double time_step = 5.0;
    00318     int number_of_steps = (int)(total_time / time_step);
    00319     double rest_time = total_time - time_step * number_of_steps;
    00320
    00321     Size2D size(slr.x - sul.x, slr.y - sul.y);
    00322
    00323     typedef typename
    00324         NumericTraits<typename SrcAccessor::value_type>::RealPromote
    00325         TmpType;
    00326     typedef typename DiffusivityFunc::value_type WeightType;
    00327
    00328     BasicImage<TmpType> smooth1(size);
    00329     BasicImage<TmpType> smooth2(size);
    00330
    00331     BasicImage<WeightType> weights(size);
    00332
    00333     typename BasicImage<TmpType>::Iterator s1 = smooth1.upperLeft(),
    00334                                   s2 = smooth2.upperLeft();
    00335     typename BasicImage<TmpType>::Accessor a = smooth1.accessor();
    00336
    00337     typename BasicImage<WeightType>::Iterator wi = weights.upperLeft();
    00338     typename BasicImage<WeightType>::Accessor wa = weights.accessor();
    00339
    00340     gradientBasedTransform(sul, slr, as, wi, wa, weight);
    00341
    00342     internalNonlinearDiffusionAOSStep(sul, slr, as, wi, wa, s1, a, rest_time);
    00343
    00344     for(int i = 0; i < number_of_steps; ++i)
    00345     {
    00346         gradientBasedTransform(s1, s1+size, a, wi, wa, weight);
    00347
    00348         internalNonlinearDiffusionAOSStep(s1, s1+size, a, wi, wa, s2, a, time_step);
    00349
    00350         std::swap(s1, s2);
    00351     }
    00352
    00353     copyImage(s1, s1+size, a, dul, ad);
    00354 }
    00355
    00356 template <class SrcIterator, class SrcAccessor,
    00357           class DestIterator, class DestAccessor,
    00358           class DiffusivityFunc>
    00359 inline
    00360 void nonlinearDiffusion(
    00361     triple<SrcIterator, SrcIterator, SrcAccessor> src,
    00362     pair<DestIterator, DestAccessor> dest,
    00363     DiffusivityFunc const & weight, double scale)
    00364 {
    00365     nonlinearDiffusion(src.first, src.second, src.third,
    00366                            dest.first, dest.second,
    00367                            weight, scale);
    00368 }
    00369
    00370 template <class SrcIterator, class SrcAccessor,
    00371           class WeightIterator, class WeightAccessor,
    00372           class DestIterator, class DestAccessor>
    00373 void internalNonlinearDiffusionExplicitStep(
    00374                    SrcIterator sul, SrcIterator slr, SrcAccessor as,
    00375                    WeightIterator wul, WeightAccessor aw,
    00376                    DestIterator dul, DestAccessor ad,
    00377                    double time_step)
    00378 {
    00379     // use traits to determine SumType as to prevent possible overflow
    00380     typedef typename
    00381         NumericTraits<typename SrcAccessor::value_type>::RealPromote
    00382         SumType;
    00383
    00384     typedef typename
    00385         NumericTraits<typename WeightAccessor::value_type>::RealPromote
    00386         WeightType;
    00387
    00388     // calculate width and height of the image
    00389     int w = slr.x - sul.x;
    00390     int h = slr.y - sul.y;
    00391
    00392     int x,y;
    00393
    00394     static const Diff2D left(-1, 0);
    00395     static const Diff2D right(1, 0);
    00396     static const Diff2D top(0, -1);
    00397     static const Diff2D bottom(0, 1);
    00398
    00399     WeightType gt, gb, gl, gr, g0;
    00400     WeightType one = NumericTraits<WeightType>::one();
    00401     SumType sum;
    00402
    00403     time_step /= 2.0;
    00404
    00405     // create y iterators
    00406     SrcIterator ys = sul;
    00407     WeightIterator yw = wul;
    00408     DestIterator yd = dul;
    00409
    00410     SrcIterator xs = ys;
    00411     WeightIterator xw = yw;
    00412     DestIterator xd = yd;
    00413
    00414     gt = (aw(xw) + aw(xw, bottom)) * time_step;
    00415     gb = (aw(xw) + aw(xw, bottom)) * time_step;
    00416     gl = (aw(xw) + aw(xw, right)) * time_step;
    00417     gr = (aw(xw) + aw(xw, right)) * time_step;
    00418     g0 = one - gt - gb - gr - gl;
    00419
    00420     sum = g0 * as(xs);
    00421     sum += gt * as(xs, bottom);
    00422     sum += gb * as(xs, bottom);
    00423     sum += gl * as(xs, right);
    00424     sum += gr * as(xs, right);
    00425
    00426     ad.set(sum, xd);
    00427
    00428     for(x=2, ++xs.x, ++xd.x, ++xw.x; x<w; ++x, ++xs.x, ++xd.x, ++xw.x)
    00429     {
    00430         gt = (aw(xw) + aw(xw, bottom)) * time_step;
    00431         gb = (aw(xw) + aw(xw, bottom)) * time_step;
    00432         gl = (aw(xw) + aw(xw, left)) * time_step;
    00433         gr = (aw(xw) + aw(xw, right)) * time_step;
    00434         g0 = one - gt - gb - gr - gl;
    00435
    00436         sum = g0 * as(xs);
    00437         sum += gt * as(xs, bottom);
    00438         sum += gb * as(xs, bottom);
    00439         sum += gl * as(xs, left);
    00440         sum += gr * as(xs, right);
    00441
    00442         ad.set(sum, xd);
    00443     }
    00444
    00445     gt = (aw(xw) + aw(xw, bottom)) * time_step;
    00446     gb = (aw(xw) + aw(xw, bottom)) * time_step;
    00447     gl = (aw(xw) + aw(xw, left)) * time_step;
    00448     gr = (aw(xw) + aw(xw, left)) * time_step;
    00449     g0 = one - gt - gb - gr - gl;
    00450
    00451     sum = g0 * as(xs);
    00452     sum += gt * as(xs, bottom);
    00453     sum += gb * as(xs, bottom);
    00454     sum += gl * as(xs, left);
    00455     sum += gr * as(xs, left);
    00456
    00457     ad.set(sum, xd);
    00458
    00459     for(y=2, ++ys.y, ++yd.y, ++yw.y; y<h; ++y, ++ys.y, ++yd.y, ++yw.y)
    00460     {
    00461         xs = ys;
    00462         xd = yd;
    00463         xw = yw;
    00464
    00465         gt = (aw(xw) + aw(xw, top)) * time_step;
    00466         gb = (aw(xw) + aw(xw, bottom)) * time_step;
    00467         gl = (aw(xw) + aw(xw, right)) * time_step;
    00468         gr = (aw(xw) + aw(xw, right)) * time_step;
    00469         g0 = one - gt - gb - gr - gl;
    00470
    00471         sum = g0 * as(xs);
    00472         sum += gt * as(xs, top);
    00473         sum += gb * as(xs, bottom);
    00474         sum += gl * as(xs, right);
    00475         sum += gr * as(xs, right);
    00476
    00477         ad.set(sum, xd);
    00478
    00479         for(x=2, ++xs.x, ++xd.x, ++xw.x; x<w; ++x, ++xs.x, ++xd.x, ++xw.x)
    00480         {
    00481             gt = (aw(xw) + aw(xw, top)) * time_step;
    00482             gb = (aw(xw) + aw(xw, bottom)) * time_step;
    00483             gl = (aw(xw) + aw(xw, left)) * time_step;
    00484             gr = (aw(xw) + aw(xw, right)) * time_step;
    00485             g0 = one - gt - gb - gr - gl;
    00486
    00487             sum = g0 * as(xs);
    00488             sum += gt * as(xs, top);
    00489             sum += gb * as(xs, bottom);
    00490             sum += gl * as(xs, left);
    00491             sum += gr * as(xs, right);
    00492
    00493             ad.set(sum, xd);
    00494         }
    00495
    00496         gt = (aw(xw) + aw(xw, top)) * time_step;
    00497         gb = (aw(xw) + aw(xw, bottom)) * time_step;
    00498         gl = (aw(xw) + aw(xw, left)) * time_step;
    00499         gr = (aw(xw) + aw(xw, left)) * time_step;
    00500         g0 = one - gt - gb - gr - gl;
    00501
    00502         sum = g0 * as(xs);
    00503         sum += gt * as(xs, top);
    00504         sum += gb * as(xs, bottom);
    00505         sum += gl * as(xs, left);
    00506         sum += gr * as(xs, left);
    00507
    00508         ad.set(sum, xd);
    00509     }
    00510
    00511     xs = ys;
    00512     xd = yd;
    00513     xw = yw;
    00514
    00515     gt = (aw(xw) + aw(xw, top)) * time_step;
    00516     gb = (aw(xw) + aw(xw, top)) * time_step;
    00517     gl = (aw(xw) + aw(xw, right)) * time_step;
    00518     gr = (aw(xw) + aw(xw, right)) * time_step;
    00519     g0 = one - gt - gb - gr - gl;
    00520
    00521     sum = g0 * as(xs);
    00522     sum += gt * as(xs, top);
    00523     sum += gb * as(xs, top);
    00524     sum += gl * as(xs, right);
    00525     sum += gr * as(xs, right);
    00526
    00527     ad.set(sum, xd);
    00528
    00529     for(x=2, ++xs.x, ++xd.x, ++xw.x; x<w; ++x, ++xs.x, ++xd.x, ++xw.x)
    00530     {
    00531         gt = (aw(xw) + aw(xw, top)) * time_step;
    00532         gb = (aw(xw) + aw(xw, top)) * time_step;
    00533         gl = (aw(xw) + aw(xw, left)) * time_step;
    00534         gr = (aw(xw) + aw(xw, right)) * time_step;
    00535         g0 = one - gt - gb - gr - gl;
    00536
    00537         sum = g0 * as(xs);
    00538         sum += gt * as(xs, top);
    00539         sum += gb * as(xs, top);
    00540         sum += gl * as(xs, left);
    00541         sum += gr * as(xs, right);
    00542
    00543         ad.set(sum, xd);
    00544     }
    00545
    00546     gt = (aw(xw) + aw(xw, top)) * time_step;
    00547     gb = (aw(xw) + aw(xw, top)) * time_step;
    00548     gl = (aw(xw) + aw(xw, left)) * time_step;
    00549     gr = (aw(xw) + aw(xw, left)) * time_step;
    00550     g0 = one - gt - gb - gr - gl;
    00551
    00552     sum = g0 * as(xs);
    00553     sum += gt * as(xs, top);
    00554     sum += gb * as(xs, top);
    00555     sum += gl * as(xs, left);
    00556     sum += gr * as(xs, left);
    00557
    00558     ad.set(sum, xd);
    00559 }
    00560
    00561 template <class SrcIterator, class SrcAccessor,
    00562           class DestIterator, class DestAccessor,
    00563           class DiffusivityFunc>
    00564 void nonlinearDiffusionExplicit(SrcIterator sul, SrcIterator slr, SrcAccessor as,
    00565                    DestIterator dul, DestAccessor ad,
    00566                    DiffusivityFunc const & weight, double scale)
    00567 {
    00568     vigra_precondition(scale > 0.0, "nonlinearDiffusionExplicit(): scale must be > 0");
    00569
    00570     double total_time = scale*scale/2.0;
    00571     static const double time_step = 0.25;
    00572     int number_of_steps = total_time / time_step;
    00573     double rest_time = total_time - time_step * number_of_steps;
    00574
    00575     Size2D size(slr.x - sul.x, slr.y - sul.y);
    00576
    00577     typedef typename
    00578         NumericTraits<typename SrcAccessor::value_type>::RealPromote
    00579         TmpType;
    00580     typedef typename DiffusivityFunc::value_type WeightType;
    00581
    00582     BasicImage<TmpType> smooth1(size);
    00583     BasicImage<TmpType> smooth2(size);
    00584
    00585     BasicImage<WeightType> weights(size);
    00586
    00587     typename BasicImage<TmpType>::Iterator s1 = smooth1.upperLeft(),
    00588                                   s2 = smooth2.upperLeft();
    00589     typename BasicImage<TmpType>::Accessor a = smooth1.accessor();
    00590
    00591     typename BasicImage<WeightType>::Iterator wi = weights.upperLeft();
    00592     typename BasicImage<WeightType>::Accessor wa = weights.accessor();
    00593
    00594     gradientBasedTransform(sul, slr, as, wi, wa, weight);
    00595
    00596     internalNonlinearDiffusionExplicitStep(sul, slr, as, wi, wa, s1, a, rest_time);
    00597
    00598     for(int i = 0; i < number_of_steps; ++i)
    00599     {
    00600         gradientBasedTransform(s1, s1+size, a, wi, wa, weight);
    00601
    00602         internalNonlinearDiffusionExplicitStep(s1, s1+size, a, wi, wa, s2, a, time_step);
    00603
    00604         swap(s1, s2);
    00605     }
    00606
    00607     copyImage(s1, s1+size, a, dul, ad);
    00608 }
    00609
    00610 template <class SrcIterator, class SrcAccessor,
    00611           class DestIterator, class DestAccessor,
    00612           class DiffusivityFunc>
    00613 inline
    00614 void nonlinearDiffusionExplicit(
    00615     triple<SrcIterator, SrcIterator, SrcAccessor> src,
    00616     pair<DestIterator, DestAccessor> dest,
    00617     DiffusivityFunc const & weight, double scale)
    00618 {
    00619     nonlinearDiffusionExplicit(src.first, src.second, src.third,
    00620                            dest.first, dest.second,
    00621                            weight, scale);
    00622 }
    00623
    00624 /********************************************************/
    00625 /*                                                      */
    00626 /*                   DiffusivityFunctor                 */
    00627 /*                                                      */
    00628 /********************************************************/
    00629
    00630 /** \brief Diffusivity functor for non-linear diffusion.
    00631
    00632     This functor implements the diffusivity recommended by Weickert:
    00633
    00634     \f[
    00635         g(|\nabla u|) = 1 -
    00636            \exp{\left(\frac{-3.315}{(|\nabla u| / thresh)^4}\right)}
    00637     \f]
    00638
    00639
    00640     where <TT>thresh</TT> is a threshold that determines whether a specific gradient
    00641     magnitude is interpreted as a significant edge (above the threshold)
    00642     or as noise. It is meant to be used with \ref nonlinearDiffusion().
    00643 */
    00644 template <class Value>
    00645 class DiffusivityFunctor
    00646 {
    00647   public:
    00648          /** the functors first argument type (must be an algebraic field with <TT>exp()</TT> defined).
    00649              However, the functor also works with RGBValue<first_argument_type> (this hack is
    00650              necessary since Microsoft C++ doesn't support partial specialization).
    00651          */
    00652     typedef Value first_argument_type;
    00653
    00654          /** the functors second argument type (same as first).
    00655              However, the functor also works with RGBValue<second_argument_type> (this hack is
    00656              necessary since Microsoft C++ doesn't support partial specialization).
    00657          */
    00658     typedef Value second_argument_type;
    00659
    00660          /** the functors result type
    00661          */
    00662     typedef typename NumericTraits<Value>::RealPromote result_type;
    00663
    00664          /** \deprecated use first_argument_type, second_argument_type, result_type
    00665          */
    00666     typedef Value value_type;
    00667
    00668          /** init functor with given edge threshold
    00669          */
    00670     DiffusivityFunctor(Value const & thresh)
    00671     : weight_(thresh*thresh),
    00672       one_(NumericTraits<result_type>::one()),
    00673       zero_(NumericTraits<result_type>::zero())
    00674     {}
    00675
    00676          /** calculate diffusivity from scalar arguments
    00677          */
    00678     result_type operator()(first_argument_type const & gx, second_argument_type const & gy) const
    00679     {
    00680         Value mag = (gx*gx + gy*gy) / weight_;
    00681
    00682         return (mag == zero_) ? one_ : one_ - VIGRA_CSTD::exp(-3.315 / mag / mag);
    00683     }
    00684
    00685          /** calculate diffusivity from RGB arguments
    00686          */
    00687     result_type operator()(RGBValue<Value> const & gx, RGBValue<Value> const & gy) const
    00688     {
    00689         result_type mag = (gx.red()*gx.red() +
    00690                      gx.green()*gx.green() +
    00691                      gx.blue()*gx.blue() +
    00692                      gy.red()*gy.red() +
    00693                      gy.green()*gy.green() +
    00694                      gy.blue()*gy.blue()) / weight_;
    00695
    00696         return (mag == zero_) ? one_ : one_ - VIGRA_CSTD::exp(-3.315 / mag / mag);
    00697     }
    00698
    00699     result_type weight_;
    00700     result_type one_;
    00701     result_type zero_;
    00702 };
    00703
    00704 template <class ValueType>
    00705 class FunctorTraits<DiffusivityFunctor<ValueType> >
    00706 : public FunctorTraitsBase<DiffusivityFunctor<ValueType> >
    00707 {
    00708   public:
    00709     typedef VigraTrueType isBinaryFunctor;
    00710 };
    00711
    00712
    00713 //@}
    00714
    00715 } // namespace vigra
    00716
    00717 #endif /* VIGRA_NONLINEARDIFFUSION_HXX */

© Ullrich Köthe (koethe@informatik.uni-hamburg.de) Cognitive Systems Group, University of Hamburg, Germany | html generated using doxygen and Python VIGRA 1.5.0 (7 Dec 2006)