source: tags/release-1.0.7/src/ATrous/atrous_3d_reconstruct.cc

#include <iostream>
#include <iomanip>
#include <math.h>
#include <ATrous/atrous.hh>
#include <Utils/utils.hh>
#include <Utils/feedback.hh>
#include <Utils/Statistics.hh>
using Statistics::madfmToSigma;

using std::endl;
using std::setw;

void atrous3DReconstruct(long &xdim, long &ydim, long &zdim, float *&input, 
                         float *&output, Param &par)
{
  /**
   *  atrous3DReconstruct(xdim, ydim, zdim, input, output, par)
   *
   *  A routine that uses the a trous wavelet method to reconstruct a 
   *   3-dimensional image cube.
   *  The Param object "par" contains all necessary info about the filter and 
   *   reconstruction parameters, although a Filter object has to be declared
   *   elsewhere previously.
   *  The input array is in "input", of dimensions "xdim"x"ydim"x"zdim", and 
   *   the reconstructed array is in "output".
   */

  extern Filter reconFilter;
  long size = xdim * ydim * zdim;
  long spatialSize = xdim * ydim;
  long mindim = xdim;
  if (ydim<mindim) mindim = ydim;
  if (zdim<mindim) mindim = zdim;
  int numScales = reconFilter.getNumScales(mindim);

  double *sigmaFactors = new double[numScales+1];
  for(int i=0;i<=numScales;i++){
    if(i<=reconFilter.maxFactor(3)) 
      sigmaFactors[i] = reconFilter.sigmaFactor(3,i);
    else sigmaFactors[i] = sigmaFactors[i-1] / sqrt(8.);
  }

  float mean,sigma,originalSigma,originalMean,oldsigma,newsigma;
  bool *isGood = new bool[size];
  float blankPixValue = par.getBlankPixVal();
  for(int pos=0;pos<size;pos++){
    isGood[pos] = !par.isBlank(input[pos]);
  }

  float *array = new float[size];
  int goodSize=0;
  for(int i=0;i<size;i++) if(isGood[i]) array[goodSize++] = input[i];
  findMedianStats(array,goodSize,originalMean,originalSigma);
  originalSigma = madfmToSigma(originalSigma);
  delete [] array;

  float *coeffs = new float[size];
  float *wavelet = new float[size];

  for(int pos=0;pos<size;pos++) output[pos]=0.;

  // Define the 3-D (separable) filter, using info from reconFilter
  int filterwidth = reconFilter.width();
  int filterHW = filterwidth/2;
  int fsize = filterwidth*filterwidth*filterwidth;
  double *filter = new double[fsize];
  for(int i=0;i<filterwidth;i++){
    for(int j=0;j<filterwidth;j++){
      for(int k=0;k<filterwidth;k++){
      filter[i +j*filterwidth + k*filterwidth*filterwidth] = 
        reconFilter.coeff(i) * reconFilter.coeff(j) * reconFilter.coeff(k);
      }
    }
  }

  // Locating the borders of the image -- ignoring BLANK pixels
  //  Only do this if flagBlankPix is true. 
  //  Otherwise use the full range of x and y.
  //  No trimming is done in the z-direction at this point.
  int *xLim1 = new int[ydim];
  for(int i=0;i<ydim;i++) xLim1[i] = 0;
  int *xLim2 = new int[ydim];
  for(int i=0;i<ydim;i++) xLim2[i] = xdim-1;
  int *yLim1 = new int[xdim];
  for(int i=0;i<xdim;i++) yLim1[i] = 0;
  int *yLim2 = new int[xdim];
  for(int i=0;i<xdim;i++) yLim2[i] = ydim-1;

  if(par.getFlagBlankPix()){
    float avGapX = 0, avGapY = 0;
    for(int row=0;row<ydim;row++){
      int ct1 = 0;
      int ct2 = xdim - 1;
      while((ct1<ct2)&&(par.isBlank(input[row*xdim+ct1]))) ct1++;
      while((ct2>ct1)&&(par.isBlank(input[row*xdim+ct2]))) ct2--;
      xLim1[row] = ct1;
      xLim2[row] = ct2;
      avGapX += ct2 - ct1 + 1;
    }
    avGapX /= float(ydim);

    for(int col=0;col<xdim;col++){
      int ct1=0;
      int ct2=ydim-1;
      while((ct1<ct2)&&(par.isBlank(input[col+xdim*ct1]))) ct1++;
      while((ct2>ct1)&&(par.isBlank(input[col+xdim*ct2]))) ct2--;
      yLim1[col] = ct1;
      yLim2[col] = ct2;
      avGapY += ct2 - ct1 + 1;
    }
    avGapY /= float(xdim);
 
    mindim = int(avGapX);
    if(avGapY < avGapX) mindim = int(avGapY);
    numScales = reconFilter.getNumScales(mindim);
  }

  float threshold;
  int iteration=0;
  newsigma = 1.e9;
  for(int i=0;i<size;i++) output[i] = 0;
  do{
    if(par.isVerbose()) std::cout << "Iteration #"<<setw(2)<<++iteration<<": ";
    // first, get the value of oldsigma, set it to the previous newsigma value
    oldsigma = newsigma;
    // we are transforming the residual array (input array first time around)
    for(int i=0;i<size;i++)  coeffs[i] = input[i] - output[i];

    int spacing = 1;
    for(int scale = 1; scale<=numScales; scale++){

      if(par.isVerbose()){
        std::cout << "Scale ";
        std::cout << setw(2)<<scale<<" / "<<setw(2)<<numScales;
        printBackSpace(13);
        std::cout << std::flush;
      }

      int pos = -1;
      for(int zpos = 0; zpos<zdim; zpos++){
        for(int ypos = 0; ypos<ydim; ypos++){
          for(int xpos = 0; xpos<xdim; xpos++){
            // loops over each pixel in the image
            pos++;

            wavelet[pos] = coeffs[pos];
            
            if(!isGood[pos] )  wavelet[pos] = 0.;
            else{

              int filterpos = -1;
              for(int zoffset=-filterHW; zoffset<=filterHW; zoffset++){
                int z = zpos + spacing*zoffset;
                if(z<0) z = -z;                 // boundary conditions are 
                if(z>=zdim) z = 2*(zdim-1) - z; //    reflection.
                
                int oldchan = z * spatialSize;
                
                for(int yoffset=-filterHW; yoffset<=filterHW; yoffset++){
                  int y = ypos + spacing*yoffset;

                  // Boundary conditions -- assume reflection at boundaries.
                  // Use limits as calculated above
                  if(yLim1[xpos]!=yLim2[xpos]){ 
                    // if these are equal we will get into an infinite loop
                    while((y<yLim1[xpos])||(y>yLim2[xpos])){
                      if(y<yLim1[xpos]) y = 2*yLim1[xpos] - y;      
                      else if(y>yLim2[xpos]) y = 2*yLim2[xpos] - y;      
                    }
                  }
                  int oldrow = y * xdim;
          
                  for(int xoffset=-filterHW; xoffset<=filterHW; xoffset++){
                    int x = xpos + spacing*xoffset;

                    // Boundary conditions -- assume reflection at boundaries.
                    // Use limits as calculated above
                    if(xLim1[ypos]!=xLim2[ypos]){
                      // if these are equal we will get into an infinite loop
                      while((x<xLim1[ypos])||(x>xLim2[ypos])){
                        if(x<xLim1[ypos]) x = 2*xLim1[ypos] - x;      
                        else if(x>xLim2[ypos]) x = 2*xLim2[ypos] - x;      
                      }
                    }

                    int oldpos = oldchan + oldrow + x;

                    filterpos++;
                   
                    if(isGood[oldpos]) 
                      wavelet[pos] -= filter[filterpos]*coeffs[oldpos];
                      
                  } //-> end of xoffset loop
                } //-> end of yoffset loop
              } //-> end of zoffset loop
            } //-> end of else{ ( from if(!isGood[pos])  )
            
          } //-> end of xpos loop
        } //-> end of ypos loop
      } //-> end of zpos loop

      // Need to do this after we've done *all* the convolving
      for(int pos=0;pos<size;pos++) coeffs[pos] = coeffs[pos] - wavelet[pos];

      // Have found wavelet coeffs for this scale -- now threshold
      if(scale>=par.getMinScale()){
        array = new float[size];
        goodSize=0;
        for(int pos=0;pos<size;pos++) 
          if(isGood[pos]) array[goodSize++] = wavelet[pos];
        findMedianStats(array,goodSize,mean,sigma);
        delete [] array;
        
        threshold = mean + 
          par.getAtrousCut()*originalSigma*sigmaFactors[scale];
        for(int pos=0;pos<size;pos++){
          if(!isGood[pos]){
            output[pos] = blankPixValue; 
            // this preserves the Blank pixel values in the output.
          }
          else if( fabs(wavelet[pos]) > threshold ){
            output[pos] += wavelet[pos];
            // only add to the output if the wavelet coefficient is significant
          }
        }
      }
 
      spacing *= 2;  // double the scale of the filter.

    } //-> end of scale loop 

    for(int pos=0;pos<size;pos++) if(isGood[pos]) output[pos] += coeffs[pos];

    array = new float[size];
    goodSize=0;
    for(int i=0;i<size;i++) {
      if(isGood[i]) array[goodSize++] = input[i] - output[i];
    }
    findMedianStats(array,goodSize,mean,newsigma);
    newsigma = madfmToSigma(newsigma);
    delete [] array;

    if(par.isVerbose()) printBackSpace(15);

  } while( (iteration==1) || 
           (fabs(oldsigma-newsigma)/newsigma > reconTolerance) );

  if(par.isVerbose()) std::cout << "Completed "<<iteration<<" iterations. ";

  delete [] xLim1;
  delete [] xLim2;
  delete [] yLim1;
  delete [] yLim2;
  delete [] coeffs;
  delete [] wavelet;
  delete [] isGood;
  delete [] filter;
  delete [] sigmaFactors;
}