[299] | 1 | // ----------------------------------------------------------------------- |
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| 2 | // atrous_2d_reconstruct.cc: 2-dimensional wavelet reconstruction. |
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| 3 | // ----------------------------------------------------------------------- |
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| 4 | // Copyright (C) 2006, Matthew Whiting, ATNF |
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| 5 | // |
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| 6 | // This program is free software; you can redistribute it and/or modify it |
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| 7 | // under the terms of the GNU General Public License as published by the |
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| 8 | // Free Software Foundation; either version 2 of the License, or (at your |
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| 9 | // option) any later version. |
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| 10 | // |
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| 11 | // Duchamp is distributed in the hope that it will be useful, but WITHOUT |
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| 12 | // ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or |
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| 13 | // FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License |
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| 14 | // for more details. |
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| 15 | // |
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| 16 | // You should have received a copy of the GNU General Public License |
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| 17 | // along with Duchamp; if not, write to the Free Software Foundation, |
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| 18 | // Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA |
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| 19 | // |
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| 20 | // Correspondence concerning Duchamp may be directed to: |
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| 21 | // Internet email: Matthew.Whiting [at] atnf.csiro.au |
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| 22 | // Postal address: Dr. Matthew Whiting |
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| 23 | // Australia Telescope National Facility, CSIRO |
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| 24 | // PO Box 76 |
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| 25 | // Epping NSW 1710 |
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| 26 | // AUSTRALIA |
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| 27 | // ----------------------------------------------------------------------- |
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[3] | 28 | #include <iostream> |
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| 29 | #include <iomanip> |
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| 30 | #include <math.h> |
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[393] | 31 | #include <duchamp/duchamp.hh> |
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| 32 | #include <duchamp/param.hh> |
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| 33 | #include <duchamp/ATrous/atrous.hh> |
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| 34 | #include <duchamp/ATrous/filter.hh> |
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| 35 | #include <duchamp/Utils/utils.hh> |
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| 36 | #include <duchamp/Utils/feedback.hh> |
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| 37 | #include <duchamp/Utils/Statistics.hh> |
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[190] | 38 | using Statistics::madfmToSigma; |
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[3] | 39 | |
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[378] | 40 | namespace duchamp |
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[3] | 41 | { |
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[86] | 42 | |
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[887] | 43 | void atrous2DReconstruct(size_t &xdim, size_t &ydim, float *&input, float *&output, Param &par) |
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[378] | 44 | { |
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[528] | 45 | /// A routine that uses the a trous wavelet method to reconstruct a |
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| 46 | /// 2-dimensional image. |
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| 47 | /// |
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| 48 | /// If there are no non-BLANK pixels (and we are testing for |
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| 49 | /// BLANKs), the reconstruction cannot be done, so we return the |
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| 50 | /// input array as the output array and give a warning message. |
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| 51 | /// |
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| 52 | /// \param xdim The length of the x-axis of the image. |
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| 53 | /// \param ydim The length of the y-axis of the image. |
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| 54 | /// \param input The input spectrum. |
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| 55 | /// \param output The returned reconstructed spectrum. This array |
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| 56 | /// needs to be declared beforehand. |
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| 57 | /// \param par The Param set:contains all necessary info about the |
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| 58 | /// filter and reconstruction parameters. |
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[3] | 59 | |
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[1026] | 60 | const float SNR_THRESH=par.getAtrousCut(); |
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| 61 | unsigned int MIN_SCALE=par.getMinScale(); |
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| 62 | unsigned int MAX_SCALE=par.getMaxScale(); |
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| 63 | |
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[846] | 64 | size_t size = xdim * ydim; |
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[1026] | 65 | size_t mindim = xdim; |
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[378] | 66 | if (ydim<mindim) mindim = ydim; |
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[1026] | 67 | |
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[884] | 68 | unsigned int numScales = par.filter().getNumScales(mindim); |
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[1026] | 69 | if((MAX_SCALE>0)&&(MAX_SCALE<=numScales)) |
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| 70 | MAX_SCALE = std::min(MAX_SCALE,numScales); |
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| 71 | else{ |
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| 72 | if((MAX_SCALE!=0)) |
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| 73 | DUCHAMPWARN("Reading parameters","The requested value of the parameter scaleMax, \"" << par.getMaxScale() << "\" is outside the allowed range (1-"<< numScales <<") -- setting to " << numScales); |
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| 74 | MAX_SCALE = numScales; |
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| 75 | par.setMaxScale(MAX_SCALE); |
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| 76 | } |
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| 77 | |
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[378] | 78 | double *sigmaFactors = new double[numScales+1]; |
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[846] | 79 | for(size_t i=0;i<=numScales;i++){ |
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[378] | 80 | if(i<=par.filter().maxFactor(2)) |
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| 81 | sigmaFactors[i] = par.filter().sigmaFactor(2,i); |
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| 82 | else sigmaFactors[i] = sigmaFactors[i-1] / 2.; |
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| 83 | } |
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[231] | 84 | |
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[894] | 85 | float mean,originalSigma,oldsigma,newsigma; |
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[846] | 86 | size_t goodSize=0; |
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[1393] | 87 | std::vector<bool> isGood(size); |
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[846] | 88 | for(size_t pos=0;pos<size;pos++){ |
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[378] | 89 | isGood[pos] = !par.isBlank(input[pos]); |
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| 90 | if(isGood[pos]) goodSize++; |
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| 91 | } |
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[231] | 92 | |
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[378] | 93 | if(goodSize == 0){ |
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| 94 | // There are no good pixels -- everything is BLANK for some reason. |
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| 95 | // Return the input array as the output, and give a warning message. |
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[231] | 96 | |
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[846] | 97 | for(size_t pos=0;pos<size; pos++) output[pos] = input[pos]; |
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[378] | 98 | |
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[913] | 99 | DUCHAMPWARN("2D Reconstruction","There are no good pixels to be reconstructed -- all are BLANK. Returning input array."); |
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[378] | 100 | } |
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| 101 | else{ |
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| 102 | // Otherwise, all is good, and we continue. |
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[231] | 103 | |
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[849] | 104 | // findMedianStats(input,goodSize,isGood,originalMean,originalSigma); |
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| 105 | // originalSigma = madfmToSigma(originalSigma); |
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| 106 | if(par.getFlagRobustStats()) |
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| 107 | originalSigma = madfmToSigma(findMADFM(input,isGood,size)); |
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| 108 | else |
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[889] | 109 | originalSigma = findStddev<float>(input,isGood,size); |
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[3] | 110 | |
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[378] | 111 | float *coeffs = new float[size]; |
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| 112 | float *wavelet = new float[size]; |
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[849] | 113 | // float *residual = new float[size]; |
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[3] | 114 | |
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[846] | 115 | for(size_t pos=0;pos<size;pos++) output[pos]=0.; |
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[3] | 116 | |
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[846] | 117 | unsigned int filterwidth = par.filter().width(); |
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[378] | 118 | int filterHW = filterwidth/2; |
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| 119 | double *filter = new double[filterwidth*filterwidth]; |
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[846] | 120 | for(size_t i=0;i<filterwidth;i++){ |
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| 121 | for(size_t j=0;j<filterwidth;j++){ |
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[378] | 122 | filter[i*filterwidth+j] = par.filter().coeff(i) * par.filter().coeff(j); |
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| 123 | } |
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[231] | 124 | } |
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[3] | 125 | |
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[908] | 126 | // long *xLim1 = new long[ydim]; |
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| 127 | // for(size_t i=0;i<ydim;i++) xLim1[i] = 0; |
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| 128 | // long *yLim1 = new long[xdim]; |
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| 129 | // for(size_t i=0;i<xdim;i++) yLim1[i] = 0; |
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| 130 | // long *xLim2 = new long[ydim]; |
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| 131 | // for(size_t i=0;i<ydim;i++) xLim2[i] = xdim-1; |
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| 132 | // long *yLim2 = new long[xdim]; |
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| 133 | // for(size_t i=0;i<xdim;i++) yLim2[i] = ydim-1; |
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[3] | 134 | |
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[908] | 135 | // if(par.getFlagBlankPix()){ |
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| 136 | // float avGapX = 0, avGapY = 0; |
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| 137 | // for(size_t row=0;row<ydim;row++){ |
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| 138 | // size_t ct1 = 0; |
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| 139 | // size_t ct2 = xdim - 1; |
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| 140 | // while((ct1<ct2)&&(par.isBlank(input[row*xdim+ct1]))) ct1++; |
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| 141 | // while((ct2>ct1)&&(par.isBlank(input[row*xdim+ct2]))) ct2--; |
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| 142 | // xLim1[row] = ct1; |
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| 143 | // xLim2[row] = ct2; |
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| 144 | // avGapX += ct2 - ct1; |
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| 145 | // } |
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| 146 | // avGapX /= float(ydim); |
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[103] | 147 | |
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[908] | 148 | // for(size_t col=0;col<xdim;col++){ |
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| 149 | // size_t ct1=0; |
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| 150 | // size_t ct2=ydim-1; |
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| 151 | // while((ct1<ct2)&&(par.isBlank(input[col+xdim*ct1]))) ct1++; |
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| 152 | // while((ct2>ct1)&&(par.isBlank(input[col+xdim*ct2]))) ct2--; |
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| 153 | // yLim1[col] = ct1; |
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| 154 | // yLim2[col] = ct2; |
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| 155 | // avGapY += ct2 - ct1; |
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| 156 | // } |
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| 157 | // avGapY /= float(xdim); |
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[103] | 158 | |
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[908] | 159 | // // if(avGapX < mindim) mindim = int(avGapX); |
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| 160 | // // if(avGapY < mindim) mindim = int(avGapY); |
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| 161 | // // numScales = par.filter().getNumScales(mindim); |
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| 162 | // } |
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[3] | 163 | |
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[378] | 164 | float threshold; |
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| 165 | int iteration=0; |
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| 166 | newsigma = 1.e9; |
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[846] | 167 | for(size_t i=0;i<size;i++) output[i] = 0; |
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[378] | 168 | do{ |
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| 169 | if(par.isVerbose()) { |
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| 170 | std::cout << "Iteration #"<<std::setw(2)<<++iteration<<":"; |
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[1384] | 171 | printBackSpace(std::cout,13); |
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[231] | 172 | } |
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| 173 | |
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[378] | 174 | // first, get the value of oldsigma and set it to the previous |
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| 175 | // newsigma value |
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| 176 | oldsigma = newsigma; |
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| 177 | // we are transforming the residual array |
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[846] | 178 | for(size_t i=0;i<size;i++) coeffs[i] = input[i] - output[i]; |
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[378] | 179 | |
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| 180 | int spacing = 1; |
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[846] | 181 | for(unsigned int scale = 1; scale<numScales; scale++){ |
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[378] | 182 | |
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| 183 | if(par.isVerbose()){ |
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| 184 | std::cout << "Scale "; |
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| 185 | std::cout << std::setw(2)<<scale<<" / "<<std::setw(2)<<numScales; |
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[1384] | 186 | printBackSpace(std::cout,13); |
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[378] | 187 | std::cout <<std::flush; |
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| 188 | } |
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| 189 | |
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[846] | 190 | for(unsigned long ypos = 0; ypos<ydim; ypos++){ |
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| 191 | for(unsigned long xpos = 0; xpos<xdim; xpos++){ |
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[378] | 192 | // loops over each pixel in the image |
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[846] | 193 | size_t pos = ypos*xdim + xpos; |
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[3] | 194 | |
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[378] | 195 | wavelet[pos] = coeffs[pos]; |
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[3] | 196 | |
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[378] | 197 | if(!isGood[pos]) wavelet[pos] = 0.; |
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| 198 | else{ |
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[3] | 199 | |
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[908] | 200 | size_t filterpos = 0; |
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[378] | 201 | for(int yoffset=-filterHW; yoffset<=filterHW; yoffset++){ |
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[846] | 202 | long y = long(ypos) + spacing*yoffset; |
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[908] | 203 | while((y<0)||(y>=long(ydim))){ |
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| 204 | // boundary conditions are reflection. |
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| 205 | if(y<0) y = 0 - y; |
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| 206 | else if(y>=long(ydim)) y = 2*(ydim-1) - y; |
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| 207 | } |
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[231] | 208 | // Boundary conditions -- assume reflection at boundaries. |
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| 209 | // Use limits as calculated above |
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[378] | 210 | // if(yLim1[xpos]!=yLim2[xpos]){ |
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| 211 | // // if these are equal we will get into an infinite loop here |
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| 212 | // while((y<yLim1[xpos])||(y>yLim2[xpos])){ |
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| 213 | // if(y<yLim1[xpos]) y = 2*yLim1[xpos] - y; |
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| 214 | // else if(y>yLim2[xpos]) y = 2*yLim2[xpos] - y; |
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[231] | 215 | // } |
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[378] | 216 | // } |
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[846] | 217 | size_t oldrow = y * xdim; |
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[378] | 218 | |
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| 219 | for(int xoffset=-filterHW; xoffset<=filterHW; xoffset++){ |
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[846] | 220 | long x = long(xpos) + spacing*xoffset; |
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[908] | 221 | while((x<0)||(x>=long(xdim))){ |
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| 222 | // boundary conditions are reflection. |
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| 223 | if(x<0) x = 0 - x; |
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| 224 | else if(x>=long(xdim)) x = 2*(xdim-1) - x; |
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| 225 | } |
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[378] | 226 | // Boundary conditions -- assume reflection at boundaries. |
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| 227 | // Use limits as calculated above |
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| 228 | // if(xLim1[ypos]!=xLim2[ypos]){ |
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| 229 | // // if these are equal we will get into an infinite loop here |
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| 230 | // while((x<xLim1[ypos])||(x>xLim2[ypos])){ |
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| 231 | // if(x<xLim1[ypos]) x = 2*xLim1[ypos] - x; |
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| 232 | // else if(x>xLim2[ypos]) x = 2*xLim2[ypos] - x; |
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| 233 | // } |
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| 234 | // } |
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[3] | 235 | |
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[846] | 236 | size_t oldpos = oldrow + x; |
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[3] | 237 | |
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[908] | 238 | // float oldCoeff; |
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| 239 | // if((y>=yLim1[xpos])&&(y<=yLim2[xpos])&& |
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| 240 | // (x>=xLim1[ypos])&&(x<=xLim2[ypos]) ) |
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| 241 | // oldCoeff = coeffs[oldpos]; |
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| 242 | // else oldCoeff = 0.; |
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[3] | 243 | |
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[908] | 244 | // if(isGood[pos]) wavelet[pos] -= filter[filterpos] * oldCoeff; |
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| 245 | // // wavelet[pos] -= filter[filterpos] * coeffs[oldpos]; |
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| 246 | if(isGood[pos]) |
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| 247 | wavelet[pos] -= filter[filterpos] * coeffs[oldpos]; |
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| 248 | |
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[378] | 249 | filterpos++; |
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[3] | 250 | |
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[378] | 251 | } //-> end of xoffset loop |
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| 252 | } //-> end of yoffset loop |
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| 253 | } //-> end of else{ ( from if(!isGood[pos]) ) |
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[3] | 254 | |
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[378] | 255 | } //-> end of xpos loop |
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| 256 | } //-> end of ypos loop |
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[3] | 257 | |
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[378] | 258 | // Need to do this after we've done *all* the convolving |
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[846] | 259 | for(size_t pos=0;pos<size;pos++) coeffs[pos] = coeffs[pos] - wavelet[pos]; |
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[3] | 260 | |
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[378] | 261 | // Have found wavelet coeffs for this scale -- now threshold |
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[1026] | 262 | if(scale>=MIN_SCALE && scale <=MAX_SCALE){ |
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[849] | 263 | // findMedianStats(wavelet,goodSize,isGood,mean,sigma); |
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| 264 | if(par.getFlagRobustStats()) |
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[889] | 265 | mean = findMedian<float>(wavelet,isGood,size); |
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[849] | 266 | else |
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[889] | 267 | mean= findMean<float>(wavelet,isGood,size); |
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[3] | 268 | |
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[1026] | 269 | threshold = mean + SNR_THRESH * originalSigma * sigmaFactors[scale]; |
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[846] | 270 | for(size_t pos=0;pos<size;pos++){ |
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[378] | 271 | if(!isGood[pos]) output[pos] = input[pos]; |
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| 272 | // preserve the Blank pixel values in the output. |
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| 273 | else if( fabs(wavelet[pos]) > threshold ) |
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| 274 | output[pos] += wavelet[pos]; |
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| 275 | } |
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[231] | 276 | } |
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[378] | 277 | spacing *= 2; |
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[3] | 278 | |
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[378] | 279 | } // END OF LOOP OVER SCALES |
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[3] | 280 | |
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[846] | 281 | for(size_t pos=0;pos<size;pos++) if(isGood[pos]) output[pos] += coeffs[pos]; |
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[3] | 282 | |
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[849] | 283 | // for(size_t i=0;i<size;i++) residual[i] = input[i] - output[i]; |
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| 284 | // findMedianStats(residual,goodSize,isGood,mean,newsigma); |
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| 285 | // findMedianStatsDiff(input,output,size,isGood,mean,newsigma); |
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| 286 | // newsigma = madfmToSigma(newsigma); |
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| 287 | if(par.getFlagRobustStats()) |
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| 288 | newsigma = madfmToSigma(findMADFMDiff(input,output,isGood,size)); |
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| 289 | else |
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[889] | 290 | newsigma = findStddevDiff<float>(input,output,isGood,size); |
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[849] | 291 | |
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[1384] | 292 | if(par.isVerbose()) printBackSpace(std::cout,15); |
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[3] | 293 | |
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[378] | 294 | } while( (iteration==1) || |
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[1026] | 295 | (fabs(oldsigma-newsigma)/newsigma > par.getReconConvergence()) ); |
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[3] | 296 | |
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[378] | 297 | if(par.isVerbose()) std::cout << "Completed "<<iteration<<" iterations. "; |
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[3] | 298 | |
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[908] | 299 | // delete [] xLim1; |
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| 300 | // delete [] xLim2; |
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| 301 | // delete [] yLim1; |
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| 302 | // delete [] yLim2; |
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[378] | 303 | delete [] filter; |
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| 304 | delete [] coeffs; |
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| 305 | delete [] wavelet; |
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[849] | 306 | // delete [] residual; |
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[231] | 307 | |
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[378] | 308 | } |
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| 309 | |
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| 310 | delete [] sigmaFactors; |
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[231] | 311 | } |
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[378] | 312 | |
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[3] | 313 | } |
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