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#ifndef VIGRA_NOISE_NORMALIZATION_HXX
#define VIGRA_NOISE_NORMALIZATION_HXX

#include "utilities.hxx"
#include "tinyvector.hxx"
#include "stdimage.hxx"
#include "transformimage.hxx"
#include "combineimages.hxx"
#include "localminmax.hxx"
#include "functorexpression.hxx"
#include "numerictraits.hxx"
#include "separableconvolution.hxx"
#include "linear_solve.hxx"
#include "array_vector.hxx"
#include "static_assert.hxx"
#include <algorithm>

namespace vigra {

/** \addtogroup NoiseNormalization Noise Normalization
    Estimate noise with intensity-dependent variance and transform it into additive Gaussian noise.
*/
//@{ 
                                    
/********************************************************/
/*                                                      */
/*               NoiseNormalizationOptions              */
/*                                                      */
/********************************************************/

/** \brief Pass options to one of the noise normalization functions.

    <tt>NoiseNormalizationOptions</tt>  is an argument object that holds various optional
    parameters used by the noise normalization functions. If a parameter is not explicitly
    set, a suitable default will be used.
    
    <b> Usage:</b>
    
        <b>\#include</b> \<vigra/noise_normalization.hxx\><br>
    Namespace: vigra
    
    \code
    vigra::BImage src(w,h);
    std::vector<vigra::TinyVector<double, 2> > result;
    
    ...
    vigra::noiseVarianceEstimation(srcImageRange(src), result, 
                                  vigra::NoiseNormalizationOptions().windowRadius(9).noiseVarianceInitialGuess(25.0));
    \endcode
*/
class NoiseNormalizationOptions
{
  public:
  
        /** Initialize all options with default values.
        */
    NoiseNormalizationOptions()
    : window_radius(6),
      cluster_count(10),
      noise_estimation_quantile(1.5),
      averaging_quantile(0.8),
      noise_variance_initial_guess(10.0),
      use_gradient(true)
    {}

        /** Select the noise estimation algorithm.
        
            If \a r is <tt>true</tt>, use the gradient-based noise estimator according to F&ouml;rstner (default).
            Otherwise, use an algorithm that uses the intensity values directly.
        */
    NoiseNormalizationOptions & useGradient(bool r)
    {
        use_gradient = r;
        return *this;
    }

        /** Set the window radius for a single noise estimate.
            Every window of the given size gives raise to one intensity/variance pair.<br>
            Default: 6 pixels
        */
    NoiseNormalizationOptions & windowRadius(unsigned int r)
    {
        vigra_precondition(r > 0,
            "NoiseNormalizationOptions: window radius must be > 0.");
        window_radius = r;
        return *this;
    }

        /** Set the number of clusters for non-parametric noise normalization.
            The intensity/variance pairs found are grouped into clusters before the noise
            normalization transform is computed.<br>
            Default: 10 clusters
        */
    NoiseNormalizationOptions & clusterCount(unsigned int c)
    {
        vigra_precondition(c > 0,
            "NoiseNormalizationOptions: cluster count must be > 0.");
        cluster_count = c;
        return *this;
    }

        /** Set the quantile for cluster averaging.
            After clustering, the cluster center (i.e. average noise variance as a function of the average
            intensity in the cluster) is computed using only the cluster members whose estimated variance
            is below \a quantile times the maximum variance in the cluster.<br>
            Default: 0.8<br>
            Precondition: 0 < \a quantile <= 1.0
        */
    NoiseNormalizationOptions & averagingQuantile(double quantile)
    {
        vigra_precondition(quantile > 0.0 && quantile <= 1.0,
            "NoiseNormalizationOptions: averaging quantile must be between 0 and 1.");
        averaging_quantile = quantile;
        return *this;
    }

        /** Set the operating range of the robust noise estimator.
            Intensity changes that are larger than \a quantile times the current estimate of the noise variance
            are ignored by the robust noise estimator.<br>
            Default: 1.5<br>
            Precondition: 0 < \a quantile
        */
    NoiseNormalizationOptions & noiseEstimationQuantile(double quantile)
    {
        vigra_precondition(quantile > 0.0,
            "NoiseNormalizationOptions: noise estimation quantile must be > 0.");
        noise_estimation_quantile = quantile;
        return *this;
    }

        /** Set the initial estimate of the noise variance.
            Robust noise variance estimation is an iterative procedure starting at the given value.<br>
            Default: 10.0<br>
            Precondition: 0 < \a guess
        */
    NoiseNormalizationOptions & noiseVarianceInitialGuess(double guess)
    {
        vigra_precondition(guess > 0.0,
            "NoiseNormalizationOptions: noise variance initial guess must be > 0.");
        noise_variance_initial_guess = guess;
        return *this;
    }

    unsigned int window_radius, cluster_count;
    double noise_estimation_quantile, averaging_quantile, noise_variance_initial_guess;
    bool use_gradient;
};

//@}

template <class ArgumentType, class ResultType>
class NonparametricNoiseNormalizationFunctor
{
    struct Segment
    {
        double lower, a, b, shift;
    };

    ArrayVector<Segment> segments_;

    template <class T>
    double exec(unsigned int k, T t) const
    {
        if(segments_[k].a == 0.0)
        {
            return t / VIGRA_CSTD::sqrt(segments_[k].b);
        }
        else
        {
            return 2.0 / segments_[k].a * VIGRA_CSTD::sqrt(std::max(0.0, segments_[k].a * t + segments_[k].b));
        }
    }

  public:
    typedef ArgumentType argument_type;
    typedef ResultType result_type;

    template <class Vector>
    NonparametricNoiseNormalizationFunctor(Vector const & clusters)
    : segments_(clusters.size()-1)
    {
        for(unsigned int k = 0; k<segments_.size(); ++k)
        {
            segments_[k].lower = clusters[k][0];
            segments_[k].a = (clusters[k+1][1] - clusters[k][1]) / (clusters[k+1][0] - clusters[k][0]);
            segments_[k].b = clusters[k][1] - segments_[k].a * clusters[k][0];
            // FIXME: set a to zero if it is very small
            //          - determine what 'very small' means
            //          - shouldn't the two formulas (for a == 0, a != 0) be equal in the limit a -> 0 ?

            if(k == 0)
            {
                segments_[k].shift = segments_[k].lower - exec(k, segments_[k].lower);
            }
            else
            {
                segments_[k].shift = exec(k-1, segments_[k].lower) - exec(k, segments_[k].lower) + segments_[k-1].shift;
            }
        }
    }

    result_type operator()(argument_type t) const
    {
        // find the segment
        unsigned int k = 0;
        for(; k < segments_.size(); ++k)
            if(t < segments_[k].lower)
                break;
        if(k > 0)
            --k;
        return detail::RequiresExplicitCast<ResultType>::cast(exec(k, t) + segments_[k].shift);
    }
};

template <class ArgumentType, class ResultType>
class QuadraticNoiseNormalizationFunctor
{
    double a, b, c, d, f, o;

    void init(double ia, double ib, double ic, double xmin)
    {
        a = ia;
        b = ib;
        c = ic;
        d = VIGRA_CSTD::sqrt(VIGRA_CSTD::fabs(c));
        if(c > 0.0)
        {
            o = VIGRA_CSTD::log(VIGRA_CSTD::fabs((2.0*c*xmin + b)/d + 2*VIGRA_CSTD::sqrt(c*sq(xmin) +b*xmin + a)))/d;
            f = 0.0;
        }
        else
        {
            f = VIGRA_CSTD::sqrt(b*b - 4.0*a*c);
            o = -VIGRA_CSTD::asin((2.0*c*xmin+b)/f)/d;
        }
    }

  public:
    typedef ArgumentType argument_type;
    typedef ResultType result_type;

    template <class Vector>
    QuadraticNoiseNormalizationFunctor(Vector const & clusters)
    {
        double xmin = NumericTraits<double>::max();
        Matrix<double> m(3,3), r(3, 1), l(3, 1);
        for(unsigned int k = 0; k<clusters.size(); ++k)
        {
            l(0,0) = 1.0;
            l(1,0) = clusters[k][0];
            l(2,0) = sq(clusters[k][0]);
            m += outer(l);
            r += clusters[k][1]*l;
            if(clusters[k][0] < xmin)
                xmin = clusters[k][0];
        }

        linearSolve(m, r, l);
        init(l(0,0), l(1,0), l(2,0), xmin);
    }

    result_type operator()(argument_type t) const
    {
        double r;
        if(c > 0.0)
            r = VIGRA_CSTD::log(VIGRA_CSTD::fabs((2.0*c*t + b)/d + 2.0*VIGRA_CSTD::sqrt(c*t*t +b*t + a)))/d-o;
        else
            r = -VIGRA_CSTD::asin((2.0*c*t+b)/f)/d-o;
        return detail::RequiresExplicitCast<ResultType>::cast(r);
    }
};

template <class ArgumentType, class ResultType>
class LinearNoiseNormalizationFunctor
{
    double a, b, o;

    void init(double ia, double ib, double xmin)
    {
        a = ia;
        b = ib;
        if(b != 0.0)
        {
            o = xmin - 2.0 / b * VIGRA_CSTD::sqrt(a + b * xmin);
        }
        else
        {
            o = xmin - xmin / VIGRA_CSTD::sqrt(a);
        }
    }

  public:
    typedef ArgumentType argument_type;
    typedef ResultType result_type;

    template <class Vector>
    LinearNoiseNormalizationFunctor(Vector const & clusters)
    {
        double xmin = NumericTraits<double>::max();
        Matrix<double> m(2,2), r(2, 1), l(2, 1);
        for(unsigned int k = 0; k<clusters.size(); ++k)
        {
            l(0,0) = 1.0;
            l(1,0) = clusters[k][0];
            m += outer(l);
            r += clusters[k][1]*l;
            if(clusters[k][0] < xmin)
                xmin = clusters[k][0];
        }

        linearSolve(m, r, l);
        init(l(0,0), l(1,0), xmin);
    }

    result_type operator()(argument_type t) const
    {
        double r;
        if(b != 0.0)
            r = 2.0 / b * VIGRA_CSTD::sqrt(a + b*t) + o;
        else
            r =  t / VIGRA_CSTD::sqrt(a) + o;
        return detail::RequiresExplicitCast<ResultType>::cast(r);
    }
};

#define VIGRA_NoiseNormalizationFunctor(name, type, size) \
template <class ResultType> \
class name<type, ResultType> \
{ \
    ResultType lut_[size]; \
    \
  public: \
    typedef type argument_type; \
    typedef ResultType result_type; \
     \
    template <class Vector> \
    name(Vector const & clusters) \
    { \
        name<double, ResultType> f(clusters); \
         \
        for(unsigned int k = 0; k < size; ++k) \
        { \
            lut_[k] = f(k); \
        } \
    } \
     \
    result_type operator()(argument_type t) const \
    { \
        return lut_[t]; \
    } \
};

VIGRA_NoiseNormalizationFunctor(NonparametricNoiseNormalizationFunctor, UInt8, 256)
VIGRA_NoiseNormalizationFunctor(NonparametricNoiseNormalizationFunctor, UInt16, 65536)
VIGRA_NoiseNormalizationFunctor(QuadraticNoiseNormalizationFunctor, UInt8, 256)
VIGRA_NoiseNormalizationFunctor(QuadraticNoiseNormalizationFunctor, UInt16, 65536)
VIGRA_NoiseNormalizationFunctor(LinearNoiseNormalizationFunctor, UInt8, 256)
VIGRA_NoiseNormalizationFunctor(LinearNoiseNormalizationFunctor, UInt16, 65536)

#undef VIGRA_NoiseNormalizationFunctor

namespace detail {

template <class SrcIterator, class SrcAcessor,
          class GradIterator>
bool
iterativeNoiseEstimationChi2(SrcIterator s, SrcAcessor src, GradIterator g,
                         double & mean, double & variance,
                         double robustnessThreshold, int windowRadius)
{
    double l2 = sq(robustnessThreshold);
    double countThreshold = 1.0 - VIGRA_CSTD::exp(-l2);
    double f = (1.0 - VIGRA_CSTD::exp(-l2)) / (1.0 - (1.0 + l2)*VIGRA_CSTD::exp(-l2));

    Diff2D ul(-windowRadius, -windowRadius);
    int r2 = sq(windowRadius);

    for(int iter=0; iter<100 ; ++iter) // maximum iteration 100 only for terminating
                                       // if something is wrong
    {
        double sum=0.0;
        double gsum=0.0;
        unsigned int count = 0;
        unsigned int tcount = 0;

        SrcIterator siy = s + ul;
        GradIterator giy = g + ul;
        for(int y=-windowRadius; y <= windowRadius; y++, ++siy.y, ++giy.y)
        {
            typename SrcIterator::row_iterator six = siy.rowIterator();
            typename GradIterator::row_iterator gix = giy.rowIterator();
            for(int x=-windowRadius; x <= windowRadius; x++, ++six, ++gix)
            {
                if (sq(x) + sq(y) > r2)
                    continue;

                ++tcount;
                if (*gix < l2*variance)
                {
                    sum += src(six);
                    gsum += *gix;
                    ++count;
                }
            }
        }
        if (count==0) // not homogeneous enough
            return false;

        double oldvariance = variance;
        variance= f * gsum / count;
        mean = sum / count;

        if ( closeAtTolerance(oldvariance - variance, 0.0, 1e-10))
            return (count >= tcount * countThreshold / 2.0); // sufficiently many valid points
    }
    return false; // no convergence
}

template <class SrcIterator, class SrcAcessor,
          class GradIterator>
bool
iterativeNoiseEstimationGauss(SrcIterator s, SrcAcessor src, GradIterator,
                         double & mean, double & variance,
                         double robustnessThreshold, int windowRadius)
{
    double l2 = sq(robustnessThreshold);
    double countThreshold = erf(VIGRA_CSTD::sqrt(0.5 * l2));
    double f = countThreshold / (countThreshold - VIGRA_CSTD::sqrt(2.0/M_PI*l2)*VIGRA_CSTD::exp(-l2/2.0));

    mean = src(s);

    Diff2D ul(-windowRadius, -windowRadius);
    int r2 = sq(windowRadius);

    for(int iter=0; iter<100 ; ++iter) // maximum iteration 100 only for terminating
                                       // if something is wrong
    {
        double sum = 0.0;
        double sum2 = 0.0;
        unsigned int count = 0;
        unsigned int tcount = 0;

        SrcIterator siy = s + ul;
        for(int y=-windowRadius; y <= windowRadius; y++, ++siy.y)
        {
            typename SrcIterator::row_iterator six = siy.rowIterator();
            for(int x=-windowRadius; x <= windowRadius; x++, ++six)
            {
                if (sq(x) + sq(y) > r2)
                    continue;

                ++tcount;
                if (sq(src(six) - mean) < l2*variance)
                {
                    sum += src(six);
                    sum2 += sq(src(six));
                    ++count;
                }
            }
        }
        if (count==0) // not homogeneous enough
            return false;

        double oldmean = mean;
        double oldvariance = variance;
        mean = sum / count;
        variance= f * (sum2 / count - sq(mean));

        if ( closeAtTolerance(oldmean - mean, 0.0, 1e-10) &&
             closeAtTolerance(oldvariance - variance, 0.0, 1e-10))
            return (count >= tcount * countThreshold / 2.0); // sufficiently many valid points
    }
    return false; // no convergence
}


template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
void
symmetricDifferenceSquaredMagnitude(
     SrcIterator sul, SrcIterator slr, SrcAccessor src,
     DestIterator dul, DestAccessor dest)
{
    using namespace functor;
    int w = slr.x - sul.x;
    int h = slr.y - sul.y;

    typedef typename NumericTraits<typename SrcAccessor::value_type>::RealPromote TmpType;
    typedef BasicImage<TmpType> TmpImage;

    Kernel1D<double> mask;
    mask.initSymmetricGradient();
    mask.setBorderTreatment(BORDER_TREATMENT_REFLECT);

    TmpImage dx(w, h), dy(w, h);
    separableConvolveX(srcIterRange(sul, slr, src), destImage(dx),  kernel1d(mask));
    separableConvolveY(srcIterRange(sul, slr, src), destImage(dy),  kernel1d(mask));
    combineTwoImages(srcImageRange(dx), srcImage(dy), destIter(dul, dest), Arg1()*Arg1() + Arg2()*Arg2());
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
void
findHomogeneousRegionsFoerstner(
     SrcIterator sul, SrcIterator slr, SrcAccessor src,
     DestIterator dul, DestAccessor dest,
     unsigned int windowRadius = 6, double homogeneityThreshold = 40.0)
{
    using namespace vigra::functor;
    int w = slr.x - sul.x;
    int h = slr.y - sul.y;

    typedef typename NumericTraits<typename SrcAccessor::value_type>::RealPromote TmpType;
    typedef BasicImage<TmpType> TmpImage;

    BImage btmp(w, h);
    transformImage(srcIterRange(sul, slr, src), destImage(btmp),
                    ifThenElse(Arg1() <= Param(homogeneityThreshold), Param(1), Param(0)));

    // Erosion
    discErosion(srcImageRange(btmp), destIter(dul, dest), windowRadius);
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
void
findHomogeneousRegions(
     SrcIterator sul, SrcIterator slr, SrcAccessor src,
     DestIterator dul, DestAccessor dest)
{
    localMinima(sul, slr, src, dul, dest);
}

template <class Vector1, class Vector2>
void noiseVarianceListMedianCut(Vector1 const & noise, Vector2 & clusters,
                                unsigned int maxClusterCount)
{
    typedef typename Vector2::value_type Result;

    clusters.push_back(Result(0, noise.size()));

    while(clusters.size() <= maxClusterCount)
    {
        // find biggest cluster
        unsigned int kMax = 0;
        double diffMax = 0.0;
        for(unsigned int k=0; k < clusters.size(); ++k)
        {
            int k1 = clusters[k][0], k2 = clusters[k][1]-1;
            
#if 0       // turned the "internal error" in a postcondition message
            // for the most likely case
            std::string message("noiseVarianceListMedianCut(): internal error (");
            message += std::string("k: ") + asString(k) + ", ";
            message += std::string("k1: ") + asString(k1) + ", ";
            message += std::string("k2: ") + asString(k2) + ", ";
            message += std::string("noise.size(): ") + asString(noise.size()) + ", ";
            message += std::string("clusters.size(): ") + asString(clusters.size()) + ").";
            vigra_invariant(k1 >= 0 && k1 < (int)noise.size() && k2 >= 0 && k2 < (int)noise.size(), message.c_str());
#endif
            
            vigra_postcondition(k1 >= 0 && k1 < (int)noise.size() && 
                                k2 >= 0 && k2 < (int)noise.size(), 
                "noiseVarianceClustering(): Unable to find homogeneous regions.");

            double diff = noise[k2][0] - noise[k1][0];
            if(diff > diffMax)
            {
                diffMax = diff;
                kMax = k;
            }
        }

        if(diffMax == 0.0)
            return; // all clusters have only one value

        unsigned int k1 = clusters[kMax][0],
                     k2 = clusters[kMax][1];
        unsigned int kSplit = k1 + (k2 - k1) / 2;
        clusters[kMax][1] = kSplit;
        clusters.push_back(Result(kSplit, k2));
    }
}

struct SortNoiseByMean
{
    template <class T>
    bool operator()(T const & l, T const & r) const
    {
        return l[0] < r[0];
    }
};

struct SortNoiseByVariance
{
    template <class T>
    bool operator()(T const & l, T const & r) const
    {
        return l[1] < r[1];
    }
};

template <class Vector1, class Vector2, class Vector3>
void noiseVarianceClusterAveraging(Vector1 & noise, Vector2 & clusters,
                                   Vector3 & result, double quantile)
{
    typedef typename Vector1::iterator Iter;
    typedef typename Vector3::value_type Result;

    for(unsigned int k=0; k<clusters.size(); ++k)
    {
        Iter i1 = noise.begin() + clusters[k][0];
        Iter i2 = noise.begin() + clusters[k][1];

        std::sort(i1, i2, SortNoiseByVariance());

        std::size_t size = static_cast<std::size_t>(VIGRA_CSTD::ceil(quantile*(i2 - i1)));
        if(static_cast<std::size_t>(i2 - i1) < size)
            size = i2 - i1;
        if(size < 1)
            size = 1;
        i2 = i1 + size;

        double mean = 0.0,
               variance = 0.0;
        for(; i1 < i2; ++i1)
        {
            mean += (*i1)[0];
            variance += (*i1)[1];
        }

        result.push_back(Result(mean / size, variance / size));
    }
}

template <class SrcIterator, class SrcAccessor, class BackInsertable>
void noiseVarianceEstimationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                           BackInsertable & result,
                           NoiseNormalizationOptions const & options)
{
    typedef typename BackInsertable::value_type ResultType;

    unsigned int w = slr.x - sul.x;
    unsigned int h = slr.y - sul.y;

    typedef typename NumericTraits<typename SrcAccessor::value_type>::RealPromote TmpType;
    typedef BasicImage<TmpType> TmpImage;

    TmpImage gradient(w, h);
    symmetricDifferenceSquaredMagnitude(sul, slr, src, gradient.upperLeft(), gradient.accessor());

    BImage homogeneous(w, h);
    findHomogeneousRegions(gradient.upperLeft(), gradient.lowerRight(), gradient.accessor(),
                                   homogeneous.upperLeft(), homogeneous.accessor());

    // Generate noise of each of the remaining pixels == centers of homogeneous areas (border is not used)
    unsigned int windowRadius = options.window_radius;
    for(unsigned int y=windowRadius; y<h-windowRadius; ++y)
    {
        for(unsigned int x=windowRadius; x<w-windowRadius; ++x)
        {
            if (! homogeneous(x, y))
                continue;

            Diff2D center(x, y);
            double mean = 0.0, variance = options.noise_variance_initial_guess;

            bool success;

            if(options.use_gradient)
            {
                success = iterativeNoiseEstimationChi2(sul + center, src,
                              gradient.upperLeft() + center, mean, variance,
                              options.noise_estimation_quantile, windowRadius);
            }
            else
            {
                success = iterativeNoiseEstimationGauss(sul + center, src,
                              gradient.upperLeft() + center, mean, variance,
                              options.noise_estimation_quantile, windowRadius);
            }
            if (success)
            {
                result.push_back(ResultType(mean, variance));
            }
        }
    }
}

template <class Vector, class BackInsertable>
void noiseVarianceClusteringImpl(Vector & noise, BackInsertable & result,
                           unsigned int clusterCount, double quantile)
{
    std::sort(noise.begin(), noise.end(), detail::SortNoiseByMean());

    ArrayVector<TinyVector<unsigned int, 2> > clusters;
    detail::noiseVarianceListMedianCut(noise, clusters, clusterCount);

    std::sort(clusters.begin(), clusters.end(), detail::SortNoiseByMean());

    detail::noiseVarianceClusterAveraging(noise, clusters, result, quantile);
}

template <class Functor,
          class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
bool
noiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                       DestIterator dul, DestAccessor dest,
                       NoiseNormalizationOptions const & options)
{
    ArrayVector<TinyVector<double, 2> > noiseData;
    noiseVarianceEstimationImpl(sul, slr, src, noiseData, options);

    if(noiseData.size() < 10)
        return false;

    ArrayVector<TinyVector<double, 2> > noiseClusters;

    noiseVarianceClusteringImpl(noiseData, noiseClusters,
                                  options.cluster_count, options.averaging_quantile);

    transformImage(sul, slr, src, dul, dest, Functor(noiseClusters));

    return true;
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
bool
nonparametricNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                                    DestIterator dul, DestAccessor dest,
                                    NoiseNormalizationOptions const & options,
                                    VigraTrueType /* isScalar */)
{
    typedef typename SrcAccessor::value_type SrcType;
    typedef typename DestAccessor::value_type DestType;
    return noiseNormalizationImpl<NonparametricNoiseNormalizationFunctor<SrcType, DestType> >
                                                         (sul, slr, src, dul, dest, options);
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
bool
nonparametricNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                              DestIterator dul, DestAccessor dest,
                              NoiseNormalizationOptions const & options,
                              VigraFalseType /* isScalar */)
{
    int bands = src.size(sul);
    for(int b=0; b<bands; ++b)
    {
        VectorElementAccessor<SrcAccessor> sband(b, src);
        VectorElementAccessor<DestAccessor> dband(b, dest);
        typedef typename VectorElementAccessor<SrcAccessor>::value_type SrcType;
        typedef typename VectorElementAccessor<DestAccessor>::value_type DestType;

        if(!noiseNormalizationImpl<NonparametricNoiseNormalizationFunctor<SrcType, DestType> >
                                                           (sul, slr, sband, dul, dband, options))
            return false;
    }
    return true;
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
bool
quadraticNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                                    DestIterator dul, DestAccessor dest,
                                    NoiseNormalizationOptions const & options,
                                    VigraTrueType /* isScalar */)
{
    typedef typename SrcAccessor::value_type SrcType;
    typedef typename DestAccessor::value_type DestType;
    return noiseNormalizationImpl<QuadraticNoiseNormalizationFunctor<SrcType, DestType> >
                                                         (sul, slr, src, dul, dest, options);
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
bool
quadraticNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                              DestIterator dul, DestAccessor dest,
                              NoiseNormalizationOptions const & options,
                              VigraFalseType /* isScalar */)
{
    int bands = src.size(sul);
    for(int b=0; b<bands; ++b)
    {
        VectorElementAccessor<SrcAccessor> sband(b, src);
        VectorElementAccessor<DestAccessor> dband(b, dest);
        typedef typename VectorElementAccessor<SrcAccessor>::value_type SrcType;
        typedef typename VectorElementAccessor<DestAccessor>::value_type DestType;

        if(!noiseNormalizationImpl<QuadraticNoiseNormalizationFunctor<SrcType, DestType> >
                                                           (sul, slr, sband, dul, dband, options))
            return false;
    }
    return true;
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
void
quadraticNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                              DestIterator dul, DestAccessor dest,
                              double a0, double a1, double a2,
                              VigraTrueType /* isScalar */)
{
    ArrayVector<TinyVector<double, 2> > noiseClusters;
    noiseClusters.push_back(TinyVector<double, 2>(0.0, a0));
    noiseClusters.push_back(TinyVector<double, 2>(1.0, a0 + a1 + a2));
    noiseClusters.push_back(TinyVector<double, 2>(2.0, a0 + 2.0*a1 + 4.0*a2));
    transformImage(sul, slr, src, dul, dest,
                   QuadraticNoiseNormalizationFunctor<typename SrcAccessor::value_type,
                                                   typename DestAccessor::value_type>(noiseClusters));
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
void
quadraticNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                              DestIterator dul, DestAccessor dest,
                              double a0, double a1, double a2,
                              VigraFalseType /* isScalar */)
{
    int bands = src.size(sul);
    for(int b=0; b<bands; ++b)
    {
        VectorElementAccessor<SrcAccessor> sband(b, src);
        VectorElementAccessor<DestAccessor> dband(b, dest);
        quadraticNoiseNormalizationImpl(sul, slr, sband, dul, dband, a0, a1, a2, VigraTrueType());
    }
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
bool
linearNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                                    DestIterator dul, DestAccessor dest,
                                    NoiseNormalizationOptions const & options,
                                    VigraTrueType /* isScalar */)
{
    typedef typename SrcAccessor::value_type SrcType;
    typedef typename DestAccessor::value_type DestType;
    return noiseNormalizationImpl<LinearNoiseNormalizationFunctor<SrcType, DestType> >
                                                         (sul, slr, src, dul, dest, options);
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
bool
linearNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                              DestIterator dul, DestAccessor dest,
                              NoiseNormalizationOptions const & options,
                              VigraFalseType /* isScalar */)
{
    int bands = src.size(sul);
    for(int b=0; b<bands; ++b)
    {
        VectorElementAccessor<SrcAccessor> sband(b, src);
        VectorElementAccessor<DestAccessor> dband(b, dest);
        typedef typename VectorElementAccessor<SrcAccessor>::value_type SrcType;
        typedef typename VectorElementAccessor<DestAccessor>::value_type DestType;

        if(!noiseNormalizationImpl<LinearNoiseNormalizationFunctor<SrcType, DestType> >
                                                           (sul, slr, sband, dul, dband, options))
            return false;
    }
    return true;
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
void
linearNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                              DestIterator dul, DestAccessor dest,
                              double a0, double a1,
                              VigraTrueType /* isScalar */)
{
    ArrayVector<TinyVector<double, 2> > noiseClusters;
    noiseClusters.push_back(TinyVector<double, 2>(0.0, a0));
    noiseClusters.push_back(TinyVector<double, 2>(1.0, a0 + a1));
    transformImage(sul, slr, src, dul, dest,
                   LinearNoiseNormalizationFunctor<typename SrcAccessor::value_type,
                                                   typename DestAccessor::value_type>(noiseClusters));
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
void
linearNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                              DestIterator dul, DestAccessor dest,
                              double a0, double a1,
                              VigraFalseType /* isScalar */)
{
    int bands = src.size(sul);
    for(int b=0; b<bands; ++b)
    {
        VectorElementAccessor<SrcAccessor> sband(b, src);
        VectorElementAccessor<DestAccessor> dband(b, dest);
        linearNoiseNormalizationImpl(sul, slr, sband, dul, dband, a0, a1, VigraTrueType());
    }
}

} // namespace detail

template <bool P>
struct noiseVarianceEstimation_can_only_work_on_scalar_images
: vigra::staticAssert::AssertBool<P>
{};

/** \addtogroup NoiseNormalization Noise Normalization
    Estimate noise with intensity-dependent variance and transform it into additive Gaussian noise.
*/
//@{ 
                                    
/********************************************************/
/*                                                      */
/*                noiseVarianceEstimation               */
/*                                                      */
/********************************************************/

/** \brief Determine the noise variance as a function of the image intensity.

    This operator applies an algorithm described in 
    
    W. F&ouml;rstner: <i>"Image Preprocessing for Feature Extraction in Digital Intensity, Color and Range Images"</i>, 
    Proc. Summer School on Data Analysis and the Statistical Foundations of Geomatics, 
    Lecture Notes in Earth Science, Berlin: Springer, 1999
    
    in order to estimate the noise variance as a function of the image intensity in a robust way,
    i.e. so that intensity changes due to edges do not bias the estimate. The source value type 
    (<TT>SrcAccessor::value_type</TT>) must be a scalar type which is convertible to <tt>double</tt>.
    The result is written into the \a result sequence, whose <tt>value_type</tt> must be constructible 
    from two <tt>double</tt> values. The following options can be set via the \a options object 
    (see \ref vigra::NoiseNormalizationOptions for details):<br><br>
    
    <tt>useGradient</tt>, <tt>windowRadius</tt>, <tt>noiseEstimationQuantile</tt>, <tt>noiseVarianceInitialGuess</tt>
    
    <b> Declarations:</b>
    
    pass arguments explicitly:
    \code
    namespace vigra {
        template <class SrcIterator, class SrcAccessor, class BackInsertable>
        void noiseVarianceEstimation(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                                     BackInsertable & result,
                                     NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
    }
    \endcode
    
    use argument objects in conjunction with \ref ArgumentObjectFactories :
    \code
    namespace vigra {
        template <class SrcIterator, class SrcAccessor, class BackInsertable>
        void noiseVarianceEstimation(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                                     BackInsertable & result,
                                     NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
    }
    \endcode
    
    <b> Usage:</b>
    
        <b>\#include</b> \<vigra/noise_normalization.hxx\><br>
    Namespace: vigra
    
    \code
    vigra::BImage src(w,h);
    std::vector<vigra::TinyVector<double, 2> > result;
    
    ...
    vigra::noiseVarianceEstimation(srcImageRange(src), result, 
                                  vigra::NoiseNormalizationOptions().windowRadius(9).noiseVarianceInitialGuess(25.0));
    
    // print the intensity / variance pairs found
    for(int k=0; k<result.size(); ++k)
        std::cout << "Intensity: " << result[k][0] << ", estimated variance: " << result[k][1] << std::endl;
    \endcode

    <b> Required Interface:</b>
    
    \code
    SrcIterator upperleft, lowerright;
    SrcAccessor src;
    
    typedef SrcAccessor::value_type SrcType;
    typedef NumericTraits<SrcType>::isScalar isScalar;
    assert(isScalar::asBool == true);
    
    double value = src(uperleft);
    
    BackInsertable result;
    typedef BackInsertable::value_type ResultType;    
    double intensity, variance;
    result.push_back(ResultType(intensity, variance));
    \endcode
*/
doxygen_overloaded_function(template <...> void noiseVarianceEstimation)

template <class SrcIterator, class SrcAccessor, class BackInsertable>
inline
void noiseVarianceEstimation(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                           BackInsertable & result,
                           NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
{
    typedef typename BackInsertable::value_type ResultType;
    typedef typename SrcAccessor::value_type SrcType;
    typedef typename NumericTraits<SrcType>::isScalar isScalar;

    VIGRA_STATIC_ASSERT((
        noiseVarianceEstimation_can_only_work_on_scalar_images<(isScalar::asBool)>));

    detail::noiseVarianceEstimationImpl(sul, slr, src, result, options);
}

template <class SrcIterator, class SrcAccessor, class BackInsertable>
inline
void noiseVarianceEstimation(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                           BackInsertable & result,
                           NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
{
    noiseVarianceEstimation(src.first, src.second, src.third, result, options);
}

/********************************************************/
/*                                                      */
/*                noiseVarianceClustering               */
/*                                                      */
/********************************************************/

/** \brief Determine the noise variance as a function of the image intensity and cluster the results.

    This operator first calls \ref noiseVarianceEstimation() to obtain a sequence of intensity/variance pairs,
    which are then clustered using the median cut algorithm. Then the cluster centers (i.e. average variance vs.
    average intensity) are determined and returned in the \a result sequence.
    
    In addition to the options valid for \ref noiseVarianceEstimation(), the following options can be set via 
    the \a options object (see \ref vigra::NoiseNormalizationOptions for details):<br><br>
    
    <tt>clusterCount</tt>, <tt>averagingQuantile</tt>
    
    <b> Declarations:</b>
    
    pass arguments explicitly:
    \code
    namespace vigra {
        template <class SrcIterator, class SrcAccessor, class BackInsertable>
        void noiseVarianceClustering(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                                BackInsertable & result,
                                NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
    }
    \endcode
    
    use argument objects in conjunction with \ref ArgumentObjectFactories :
    \code
    namespace vigra {
        template <class SrcIterator, class SrcAccessor, class BackInsertable>
        void noiseVarianceClustering(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                                BackInsertable & result,
                                NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
    }
    \endcode
    
    <b> Usage:</b>
    
        <b>\#include</b> \<vigra/noise_normalization.hxx\><br>
    Namespace: vigra
    
    \code
    vigra::BImage src(w,h);
    std::vector<vigra::TinyVector<double, 2> > result;
    
    ...
    vigra::noiseVarianceClustering(srcImageRange(src), result, 
                                  vigra::NoiseNormalizationOptions().windowRadius(9).noiseVarianceInitialGuess(25.0).
                                  clusterCount(15));
    
    // print the intensity / variance pairs representing the cluster centers
    for(int k=0; k<result.size(); ++k)
        std::cout << "Cluster: " << k << ", intensity: " << result[k][0] << ", estimated variance: " << result[k][1] << std::endl;
    \endcode

    <b> Required Interface:</b>
    
    same as \ref noiseVarianceEstimation()
*/
doxygen_overloaded_function(template <...> void noiseVarianceClustering)

template <class SrcIterator, class SrcAccessor, class BackInsertable>
inline
void noiseVarianceClustering(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                           BackInsertable & result,
                           NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
{
    ArrayVector<TinyVector<double, 2> > variance;
    noiseVarianceEstimation(sul, slr, src, variance, options);
    detail::noiseVarianceClusteringImpl(variance, result, options.cluster_count, options.averaging_quantile);
}

template <class SrcIterator, class SrcAccessor, class BackInsertable>
inline
void noiseVarianceClustering(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                           BackInsertable & result,
                           NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
{
    noiseVarianceClustering(src.first, src.second, src.third, result, options);
}

/********************************************************/
/*                                                      */
/*             nonparametricNoiseNormalization          */
/*                                                      */
/********************************************************/

/** \brief Noise normalization by means of an estimated non-parametric noise model.

    The original image is assumed to be corrupted by noise whose variance depends on the intensity in an unknown way.
    The present functions first calls \ref noiseVarianceClustering() to obtain a sequence of intensity/variance pairs
    (cluster centers) which estimate this dependency. The cluster centers are connected into a piecewise linear 
    function which is the inverted according to the formula derived in 
    
    W. F&ouml;rstner: <i>"Image Preprocessing for Feature Extraction in Digital Intensity, Color and Range Images"</i>, 
    Proc. Summer School on Data Analysis and the Statistical Foundations of Geomatics, 
    Lecture Notes in Earth Science, Berlin: Springer, 1999

    The inverted formula defines a pixel-wise intensity transformation whose application turns the original image
    into one that is corrupted by additive Gaussian noise with unit variance. Most subsequent algorithms will be able
    to handle this type of noise much better than the original noise.
    
    RGB and other multiband images will be processed one band at a time. The function returns <tt>true</tt> on success.
    Noise normalization will fail if the original image does not contain sufficiently homogeneous regions that
    allow robust estimation of the noise variance.
    
    The \a options object may use all options described in \ref vigra::NoiseNormalizationOptions.
    
    <b> Declarations:</b>
    
    pass arguments explicitly:
    \code
    namespace vigra {
        template <class SrcIterator, class SrcAccessor,
                  class DestIterator, class DestAccessor>
        bool nonparametricNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                                             DestIterator dul, DestAccessor dest,
                                             NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
    }
    \endcode
    
    use argument objects in conjunction with \ref ArgumentObjectFactories :
    \code
    namespace vigra {
        template <class SrcIterator, class SrcAccessor,
                  class DestIterator, class DestAccessor>
        bool nonparametricNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                                             pair<DestIterator, DestAccessor> dest,
                                             NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
    }
    \endcode
    
    <b> Usage:</b>
    
        <b>\#include</b> \<vigra/noise_normalization.hxx\><br>
    Namespace: vigra
    
    \code
    vigra::BRGBImage src(w,h), dest(w, h);
    
    ...
    vigra::nonparametricNoiseNormalization(srcImageRange(src), destImage(dest), 
                                           vigra::NoiseNormalizationOptions().windowRadius(9).noiseVarianceInitialGuess(25.0).
                                           clusterCount(15));
    \endcode

    <b> Required Interface:</b>
    
    same as \ref noiseVarianceEstimation()
*/
doxygen_overloaded_function(template <...> bool nonparametricNoiseNormalization)

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
inline bool
nonparametricNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                                DestIterator dul, DestAccessor dest,
                                NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
{
    typedef typename SrcAccessor::value_type SrcType;

    return detail::nonparametricNoiseNormalizationImpl(sul, slr, src, dul, dest, options,
                                         typename NumericTraits<SrcType>::isScalar());
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
inline
bool nonparametricNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                                     pair<DestIterator, DestAccessor> dest,
                                     NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
{
    return nonparametricNoiseNormalization(src.first, src.second, src.third, dest.first, dest.second, options);
}

/********************************************************/
/*                                                      */
/*               quadraticNoiseNormalization            */
/*                                                      */
/********************************************************/

/** \brief Noise normalization by means of an estimated quadratic noise model.

    This function works in the same way as \ref nonparametricNoiseNormalization() with the exception of the 
    model for the dependency between intensity and noise variance: it assumes that this dependency is a 
    quadratic function rather than a piecewise linear function. If the quadratic model is applicable, it leads
    to a somewhat smoother transformation.
    
    <b> Declarations:</b>
    
    pass arguments explicitly:
    \code
    namespace vigra {
        template <class SrcIterator, class SrcAccessor,
                  class DestIterator, class DestAccessor>
        bool quadraticNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                                         DestIterator dul, DestAccessor dest,
                                         NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
    }
    \endcode
    
    use argument objects in conjunction with \ref ArgumentObjectFactories :
    \code
    namespace vigra {
        template <class SrcIterator, class SrcAccessor,
                  class DestIterator, class DestAccessor>
        bool quadraticNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                                         pair<DestIterator, DestAccessor> dest,
                                         NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
    }
    \endcode
    
    <b> Usage:</b>
    
        <b>\#include</b> \<vigra/noise_normalization.hxx\><br>
    Namespace: vigra
    
    \code
    vigra::BRGBImage src(w,h), dest(w, h);
    
    ...
    vigra::quadraticNoiseNormalization(srcImageRange(src), destImage(dest), 
                                       vigra::NoiseNormalizationOptions().windowRadius(9).noiseVarianceInitialGuess(25.0).
                                       clusterCount(15));
    \endcode

    <b> Required Interface:</b>
    
    same as \ref noiseVarianceEstimation()
*/
doxygen_overloaded_function(template <...> bool quadraticNoiseNormalization)

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
inline bool
quadraticNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                                DestIterator dul, DestAccessor dest,
                                NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
{
    typedef typename SrcAccessor::value_type SrcType;

    return detail::quadraticNoiseNormalizationImpl(sul, slr, src, dul, dest, options,
                                         typename NumericTraits<SrcType>::isScalar());
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
inline
bool quadraticNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                                     pair<DestIterator, DestAccessor> dest,
                                     NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
{
    return quadraticNoiseNormalization(src.first, src.second, src.third, dest.first, dest.second, options);
}

/********************************************************/
/*                                                      */
/*               quadraticNoiseNormalization            */
/*                                                      */
/********************************************************/

/** \brief Noise normalization by means of a given quadratic noise model.

    This function works similar to \ref nonparametricNoiseNormalization() with the exception that the 
    functional dependency of the noise variance from the intensity is given (by a quadratic function)
    rather than estimated:
    
    \code
    variance = a0 + a1 * intensity + a2 * sq(intensity)
    \endcode
    
    <b> Declarations:</b>
    
    pass arguments explicitly:
    \code
    namespace vigra {
        template <class SrcIterator, class SrcAccessor,
                  class DestIterator, class DestAccessor>
        void quadraticNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                                         DestIterator dul, DestAccessor dest,
                                         double a0, double a1, double a2);
    }
    \endcode
    
    use argument objects in conjunction with \ref ArgumentObjectFactories :
    \code
    namespace vigra {
        template <class SrcIterator, class SrcAccessor,
                  class DestIterator, class DestAccessor>
        void quadraticNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                                        pair<DestIterator, DestAccessor> dest,
                                        double a0, double a1, double a2);
    }
    \endcode
    
    <b> Usage:</b>
    
        <b>\#include</b> \<vigra/noise_normalization.hxx\><br>
    Namespace: vigra
    
    \code
    vigra::BRGBImage src(w,h), dest(w, h);
    
    ...
    vigra::quadraticNoiseNormalization(srcImageRange(src), destImage(dest), 
                                       100, 0.02, 1e-6);
    \endcode

    <b> Required Interface:</b>
    
    The source value type must be convertible to <tt>double</tt> or must be a vector whose elements 
    are convertible to <tt>double</tt>. Likewise, the destination type must be assignable from <tt>double</tt>
    or a vector whose elements are assignable from <tt>double</tt>.
*/
doxygen_overloaded_function(template <...> void quadraticNoiseNormalization)

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
inline void
quadraticNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                            DestIterator dul, DestAccessor dest,
                            double a0, double a1, double a2)
{
    typedef typename SrcAccessor::value_type SrcType;

    detail::quadraticNoiseNormalizationImpl(sul, slr, src, dul, dest, a0, a1, a2,
                                         typename NumericTraits<SrcType>::isScalar());
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
inline
void quadraticNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                                 pair<DestIterator, DestAccessor> dest,
                                 double a0, double a1, double a2)
{
    quadraticNoiseNormalization(src.first, src.second, src.third, dest.first, dest.second, a0, a1, a2);
}

/********************************************************/
/*                                                      */
/*                linearNoiseNormalization              */
/*                                                      */
/********************************************************/

/** \brief Noise normalization by means of an estimated linear noise model.

    This function works in the same way as \ref nonparametricNoiseNormalization() with the exception of the 
    model for the dependency between intensity and noise variance: it assumes that this dependency is a 
    linear function rather than a piecewise linear function. If the linear model is applicable, it leads
    to a very simple transformation which is similar to the familiar gamma correction.
    
    <b> Declarations:</b>
    
    pass arguments explicitly:
    \code
    namespace vigra {
        template <class SrcIterator, class SrcAccessor,
                class DestIterator, class DestAccessor>
        bool linearNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                                      DestIterator dul, DestAccessor dest,
                                      NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
    }
    \endcode
    
    use argument objects in conjunction with \ref ArgumentObjectFactories :
    \code
    namespace vigra {
        template <class SrcIterator, class SrcAccessor,
                  class DestIterator, class DestAccessor>
        bool linearNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                                      pair<DestIterator, DestAccessor> dest,
                                      NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
    }
    \endcode
    
    <b> Usage:</b>
    
        <b>\#include</b> \<vigra/noise_normalization.hxx\><br>
    Namespace: vigra
    
    \code
    vigra::BRGBImage src(w,h), dest(w, h);
    
    ...
    vigra::linearNoiseNormalization(srcImageRange(src), destImage(dest), 
                                    vigra::NoiseNormalizationOptions().windowRadius(9).noiseVarianceInitialGuess(25.0).
                                    clusterCount(15));
    \endcode

    <b> Required Interface:</b>
    
    same as \ref noiseVarianceEstimation()
*/
doxygen_overloaded_function(template <...> bool linearNoiseNormalization)

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
inline bool
linearNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                                DestIterator dul, DestAccessor dest,
                                NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
{
    typedef typename SrcAccessor::value_type SrcType;

    return detail::linearNoiseNormalizationImpl(sul, slr, src, dul, dest, options,
                                         typename NumericTraits<SrcType>::isScalar());
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
inline
bool linearNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                                     pair<DestIterator, DestAccessor> dest,
                                     NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
{
    return linearNoiseNormalization(src.first, src.second, src.third, dest.first, dest.second, options);
}

/********************************************************/
/*                                                      */
/*                 linearNoiseNormalization             */
/*                                                      */
/********************************************************/

/** \brief Noise normalization by means of a given linear noise model.

    This function works similar to \ref nonparametricNoiseNormalization() with the exception that the 
    functional dependency of the noise variance from the intensity is given (as a linear function) 
    rather than estimated:
    
    \code
    variance = a0 + a1 * intensity
    \endcode
    
    <b> Declarations:</b>
    
    pass arguments explicitly:
    \code
    namespace vigra {
        template <class SrcIterator, class SrcAccessor,
                  class DestIterator, class DestAccessor>
        void linearNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                                      DestIterator dul, DestAccessor dest,
                                      double a0, double a1);
    }
    \endcode
    
    use argument objects in conjunction with \ref ArgumentObjectFactories :
    \code
    namespace vigra {
        template <class SrcIterator, class SrcAccessor,
                  class DestIterator, class DestAccessor>
        void linearNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                                      pair<DestIterator, DestAccessor> dest,
                                      double a0, double a1);
    }
    \endcode
    
    <b> Usage:</b>
    
        <b>\#include</b> \<vigra/noise_normalization.hxx\><br>
    Namespace: vigra
    
    \code
    vigra::BRGBImage src(w,h), dest(w, h);
    
    ...
    vigra::linearNoiseNormalization(srcImageRange(src), destImage(dest), 
                                    100, 0.02);
    \endcode

    <b> Required Interface:</b>
    
    The source value type must be convertible to <tt>double</tt> or must be a vector whose elements 
    are convertible to <tt>double</tt>. Likewise, the destination type must be assignable from <tt>double</tt>
    or a vector whose elements are assignable from <tt>double</tt>.
*/
doxygen_overloaded_function(template <...> void linearNoiseNormalization)

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
inline
void linearNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
                              DestIterator dul, DestAccessor dest,
                              double a0, double a1)
{
    typedef typename SrcAccessor::value_type SrcType;

    detail::linearNoiseNormalizationImpl(sul, slr, src, dul, dest, a0, a1,
                                         typename NumericTraits<SrcType>::isScalar());
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor>
inline
void linearNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                              pair<DestIterator, DestAccessor> dest,
                              double a0, double a1)
{
    linearNoiseNormalization(src.first, src.second, src.third, dest.first, dest.second, a0, a1);
}

//@}

} // namespace vigra

#endif // VIGRA_NOISE_NORMALIZATION_HXX
