source: tags/release-1.1.5/src/ATrous/atrous_3d_reconstruct.cc @ 1441

// -----------------------------------------------------------------------
// atrous_3d_reconstruct.cc: 3-dimensional wavelet reconstruction.
// -----------------------------------------------------------------------
// Copyright (C) 2006, Matthew Whiting, ATNF
//
// This program is free software; you can redistribute it and/or modify it
// under the terms of the GNU General Public License as published by the
// Free Software Foundation; either version 2 of the License, or (at your
// option) any later version.
//
// Duchamp is distributed in the hope that it will be useful, but WITHOUT
// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
// FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License
// for more details.
//
// You should have received a copy of the GNU General Public License
// along with Duchamp; if not, write to the Free Software Foundation,
// Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA
//
// Correspondence concerning Duchamp may be directed to:
//    Internet email: Matthew.Whiting [at] atnf.csiro.au
//    Postal address: Dr. Matthew Whiting
//                    Australia Telescope National Facility, CSIRO
//                    PO Box 76
//                    Epping NSW 1710
//                    AUSTRALIA
// -----------------------------------------------------------------------
#include <iostream>
#include <iomanip>
#include <math.h>
#include <duchamp/duchamp.hh>
#include <duchamp/param.hh>
#include <duchamp/ATrous/atrous.hh>
#include <duchamp/ATrous/filter.hh>
#include <duchamp/Utils/utils.hh>
#include <duchamp/Utils/feedback.hh>
#include <duchamp/Utils/Statistics.hh>
using Statistics::madfmToSigma;

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

namespace duchamp
{

  void atrous3DReconstruct(long &xdim, long &ydim, long &zdim, float *&input, 
                           float *&output, Param &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.
     *
     *  If there are no non-BLANK pixels (and we are testing for
     *  BLANKs), the reconstruction cannot be done, so we return the
     *  input array as the output array and give a warning message.
     *
     *  \param xdim The length of the x-axis.
     *  \param ydim The length of the y-axis.
     *  \param zdim The length of the z-axis.
     *  \param input The input spectrum.
     *  \param output The returned reconstructed spectrum. This array needs to be declared beforehand.
     *  \param par The Param set.
     */

    long size = xdim * ydim * zdim;
    long spatialSize = xdim * ydim;
    long mindim = xdim;
    if (ydim<mindim) mindim = ydim;
    if (zdim<mindim) mindim = zdim;
    int numScales = par.filter().getNumScales(mindim);

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

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

    if(goodSize == 0){
      // There are no good pixels -- everything is BLANK for some reason.
      // Return the input array as the output, and give a warning message.

      for(int pos=0;pos<xdim; pos++) output[pos] = input[pos];

      duchampWarning("3D Reconstruction",
                     "There are no good pixels to be reconstructed -- all are BLANK.\nPerhaps you need to try this with flagTrim=false.\nReturning input array.\n");
    }
    else{
      // Otherwise, all is good, and we continue.


      float *array = new float[size];
      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 par.filter()
      int filterwidth = par.filter().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] = 
              par.filter().coeff(i) * par.filter().coeff(j) * par.filter().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 = par.filter().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] = input[pos]; 
                // 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 [] filter;
      delete [] coeffs;
      delete [] wavelet;

    }

    delete [] isGood;
    delete [] sigmaFactors;
  }

}