class AddFloatToCharScaled{
public:
- AddFloatToCharScaled(float scale):_scale(scale){}
- inline float operator()(float a,uchar b){
- return a+_scale*((float)b);
+ AddFloatToCharScaled(double scale):_scale(scale){}
+ inline double operator()(double a,uchar b){
+ return a+_scale*((double)b);
}
private:
- float _scale;
+ double _scale;
};
void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda, int niters){
CV_Assert(observations.size()>0 && niters>0 && lambda>0);
- const float L2 = 8.0f, tau = 0.02f, sigma = 1./(L2*tau), theta = 1.f;
- float clambda = (float)lambda;
- float s=0;
- const int workdepth = CV_32F;
+ const double L2 = 8.0, tau = 0.02, sigma = 1./(L2*tau), theta = 1.0;
+ double clambda = (double)lambda;
+ double s=0;
+ const int workdepth = CV_64F;
int i, x, y, rows=observations[0].rows, cols=observations[0].cols,count;
for(i=1;i<(int)observations.size();i++){
Mat X, P = Mat::zeros(rows, cols, CV_MAKETYPE(workdepth, 2));
observations[0].convertTo(X, workdepth, 1./255);
- std::vector< Mat_<float> > Rs(observations.size());
+ std::vector< Mat_<double> > Rs(observations.size());
for(count=0;count<(int)Rs.size();count++){
Rs[count]=Mat::zeros(rows,cols,workdepth);
}
for( i = 0; i < niters; i++ )
{
- float currsigma = i == 0 ? 1 + sigma : sigma;
+ double currsigma = i == 0 ? 1 + sigma : sigma;
// P_ = P + sigma*nabla(X)
// P(x,y) = P_(x,y)/max(||P(x,y)||,1)
for( y = 0; y < rows; y++ )
{
- const float* x_curr = X.ptr<float>(y);
- const float* x_next = X.ptr<float>(std::min(y+1, rows-1));
- Point2f* p_curr = P.ptr<Point2f>(y);
- float dx, dy, m;
+ const double* x_curr = X.ptr<double>(y);
+ const double* x_next = X.ptr<double>(std::min(y+1, rows-1));
+ Point2d* p_curr = P.ptr<Point2d>(y);
+ double dx, dy, m;
for( x = 0; x < cols-1; x++ )
{
dx = (x_curr[x+1] - x_curr[x])*currsigma + p_curr[x].x;
dy = (x_next[x] - x_curr[x])*currsigma + p_curr[x].y;
- m = 1.f/std::max(std::sqrt(dx*dx + dy*dy), 1.f);
+ m = 1.0/std::max(std::sqrt(dx*dx + dy*dy), 1.0);
p_curr[x].x = dx*m;
p_curr[x].y = dy*m;
}
dy = (x_next[x] - x_curr[x])*currsigma + p_curr[x].y;
- m = 1.f/std::max(std::abs(dy), 1.f);
- p_curr[x].x = 0.f;
+ m = 1.0/std::max(std::abs(dy), 1.0);
+ p_curr[x].x = 0.0;
p_curr[x].y = dy*m;
}
//Rs = clip(Rs + sigma*(X-imgs), -clambda, clambda)
for(count=0;count<(int)Rs.size();count++){
- std::transform<MatIterator_<float>,MatConstIterator_<uchar>,MatIterator_<float>,AddFloatToCharScaled>(
+ std::transform<MatIterator_<double>,MatConstIterator_<uchar>,MatIterator_<double>,AddFloatToCharScaled>(
Rs[count].begin(),Rs[count].end(),observations[count].begin<uchar>(),
Rs[count].begin(),AddFloatToCharScaled(-sigma/255.0));
Rs[count]+=sigma*X;
for( y = 0; y < rows; y++ )
{
- float* x_curr = X.ptr<float>(y);
- const Point2f* p_curr = P.ptr<Point2f>(y);
- const Point2f* p_prev = P.ptr<Point2f>(std::max(y - 1, 0));
+ double* x_curr = X.ptr<double>(y);
+ const Point2d* p_curr = P.ptr<Point2d>(y);
+ const Point2d* p_prev = P.ptr<Point2d>(std::max(y - 1, 0));
// X1 = X + tau*(-nablaT(P))
x = 0;
for(count=0;count<(int)Rs.size();count++){
s=s+Rs[count](y,x);
}
- float x_new = x_curr[x] + tau*(p_curr[x].y - p_prev[x].y)-tau*s;
+ double x_new = x_curr[x] + tau*(p_curr[x].y - p_prev[x].y)-tau*s;
// X = X2 + theta*(X2 - X)
x_curr[x] = x_new + theta*(x_new - x_curr[x]);