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fft3dfilter_c.cpp
1770 lines (1665 loc) · 74.7 KB
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fft3dfilter_c.cpp
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//
// FFT3DFilter plugin for Avisynth 2.5 - 3D Frequency Domain filter
// pure C++ filtering functions
// v1.9.2
// Copyright(C)2004-2006 A.G.Balakhnin aka Fizick, bag@hotmail.ru, http://avisynth.org.ru
//
// This program is free software; you can redistribute it and/or modify
// it under the terms of the GNU General Public License version 2 as published by
// the Free Software Foundation.
//
// This program 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 this program; if not, write to the Free Software
// Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
//
//-----------------------------------------------------------------------------------------
#ifdef _WIN32
#define NOMINMAX
#include "Windows.h"
#endif
#include "fftwlite.h"
#include "math.h" // for sqrtf
#include <algorithm>
// since v1.7 we use outpitch instead of outwidth
//-----------------------------------------------------------------------------------------
//
void ApplyWiener2D_C(fftwf_complex *outcur, int outwidth, int outpitch, int bh,
int howmanyblocks, float sigmaSquaredNoiseNormed, float beta,
float sharpen, float sigmaSquaredSharpenMin,
float sigmaSquaredSharpenMax, float *wsharpen, float dehalo, float *wdehalo, float ht2n)
{
// this function take 25% CPU time and may be easy optimized for AMD Athlon 3DNOW assembler
float lowlimit = (beta-1)/beta; // (beta-1)/beta>=0
int h,w, block;
float psd;
float WienerFactor;
if (sharpen == 0 && dehalo == 0)// no sharpen, no dehalo
{
for (block =0; block <howmanyblocks; block++)
{
for (h=0; h<bh; h++) // middle
{
for (w=0; w<outwidth; w++) // not skip first v.1.2
{
psd = (outcur[w][0]*outcur[w][0] + outcur[w][1]*outcur[w][1]) + 1e-15f;// power spectrum density
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
outcur[w][0] *= WienerFactor; // apply filter on real part
outcur[w][1] *= WienerFactor; // apply filter on imaginary part
}
outcur += outpitch;
}
}
}
else if (sharpen != 0 && dehalo==0) // sharpen
{
for (block =0; block <howmanyblocks; block++)
{
for (h=0; h<bh; h++) // middle
{
for (w=0; w<outwidth; w++) // not skip first
{
psd = (outcur[w][0]*outcur[w][0] + outcur[w][1]*outcur[w][1]) + 1e-15f;// power spectrum density
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
WienerFactor *= 1 + sharpen*wsharpen[w]*sqrt( psd*sigmaSquaredSharpenMax/((psd + sigmaSquaredSharpenMin)*(psd + sigmaSquaredSharpenMax)) ); // sharpen factor - changed in v.1.1
outcur[w][0] *= WienerFactor; // apply filter on real part
outcur[w][1] *= WienerFactor; // apply filter on imaginary part
}
outcur += outpitch;
wsharpen += outpitch;
}
wsharpen -= outpitch*bh;
}
}
else if (sharpen == 0 && dehalo != 0)
{
for (block =0; block <howmanyblocks; block++)
{
for (h=0; h<bh; h++) // middle
{
for (w=0; w<outwidth; w++) // not skip first
{
psd = (outcur[w][0]*outcur[w][0] + outcur[w][1]*outcur[w][1]) + 1e-15f;// power spectrum density
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
WienerFactor *= (psd + ht2n)/((psd + ht2n) + dehalo*wdehalo[w] * psd );
outcur[w][0] *= WienerFactor; // apply filter on real part
outcur[w][1] *= WienerFactor; // apply filter on imaginary part
}
outcur += outpitch;
wdehalo += outpitch;
}
wdehalo -= outpitch*bh;
}
}
else if (sharpen != 0 && dehalo != 0)
{
for (block =0; block <howmanyblocks; block++)
{
for (h=0; h<bh; h++) // middle
{
for (w=0; w<outwidth; w++) // not skip first
{
psd = (outcur[w][0]*outcur[w][0] + outcur[w][1]*outcur[w][1]) + 1e-15f;// power spectrum density
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
WienerFactor *= 1 + sharpen * wsharpen[w] * sqrt(psd * sigmaSquaredSharpenMax / ((psd + sigmaSquaredSharpenMin) * (psd + sigmaSquaredSharpenMax)));
WienerFactor *= (psd + ht2n)/((psd + ht2n) + dehalo*wdehalo[w] * psd );
outcur[w][0] *= WienerFactor; // apply filter on real part
outcur[w][1] *= WienerFactor; // apply filter on imaginary part
}
outcur += outpitch;
wsharpen += outpitch;
wdehalo += outpitch;
}
wsharpen -= outpitch*bh;
wdehalo -= outpitch*bh;
}
}
}
//-------------------------------------------------------------------------------------------
//
void ApplyPattern2D_C(fftwf_complex *outcur, int outwidth, int outpitch, int bh, int howmanyblocks, float pfactor, float *pattern2d0, float beta)
{
int h,w, block;
float psd;
float patternfactor;
float *pattern2d;
float lowlimit = (beta-1)/beta; // (beta-1)/beta>=0
if (pfactor != 0)
{
for (block =0; block <howmanyblocks; block++)
{
pattern2d = pattern2d0;
for (h=0; h<bh; h++) // middle
{
for (w=0; w<outwidth; w++)
{
psd = (outcur[w][0]*outcur[w][0] + outcur[w][1]*outcur[w][1]) + 1e-15f;
patternfactor = std::max((psd - pfactor*pattern2d[w])/psd, lowlimit);
outcur[w][0] *= patternfactor;
outcur[w][1] *= patternfactor;
}
outcur += outpitch;
pattern2d += outpitch;
}
}
}
}
//
//-----------------------------------------------------------------------------------------
//
void ApplyWiener3D2_C(fftwf_complex *outcur, fftwf_complex *outprev,
int outwidth, int outpitch, int bh, int howmanyblocks,
float sigmaSquaredNoiseNormed, float beta)
{
// this function take 25% CPU time and may be easy optimized for AMD Athlon 3DNOW assembler
// return result in outprev
float lowlimit = (beta-1)/beta; // (beta-1)/beta>=0
float psd;
float WienerFactor;
float f3d0r, f3d1r, f3d0i, f3d1i;
int block;
int h,w;
for (block=0; block <howmanyblocks; block++)
{
for (h=0; h<bh; h++)
{
for (w=0; w<outwidth; w++)
{
// dft 3d (very short - 2 points)
f3d0r = outcur[w][0] + outprev[w][0]; // real 0 (sum)
f3d0i = outcur[w][1] + outprev[w][1]; // im 0 (sum)
f3d1r = outcur[w][0] - outprev[w][0]; // real 1 (dif)
f3d1i = outcur[w][1] - outprev[w][1]; // im 1 (dif)
psd = f3d0r*f3d0r + f3d0i*f3d0i + 1e-15f; // power spectrum density 0
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
f3d0r *= WienerFactor; // apply filter on real part
f3d0i *= WienerFactor; // apply filter on imaginary part
psd = f3d1r*f3d1r + f3d1i*f3d1i + 1e-15f; // power spectrum density 1
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
f3d1r *= WienerFactor; // apply filter on real part
f3d1i *= WienerFactor; // apply filter on imaginary part
// reverse dft for 2 points
outprev[w][0] = (f3d0r + f3d1r)*0.5f; // get real part
outprev[w][1] = (f3d0i + f3d1i)*0.5f; // get imaginary part
// Attention! return filtered "out" in "outprev" to preserve "out" for next step
}
outcur += outpitch;
outprev += outpitch;
}
}
}
//
//-----------------------------------------------------------------------------------------
//
void ApplyPattern3D2_C(fftwf_complex *outcur, fftwf_complex *outprev,
int outwidth, int outpitch, int bh,
int howmanyblocks, float *pattern3d, float beta)
{
// this function take 25% CPU time and may be easy optimized for AMD Athlon 3DNOW assembler
// return result in outprev
float lowlimit = (beta-1)/beta; // (beta-1)/beta>=0
float psd;
float WienerFactor;
float f3d0r, f3d1r, f3d0i, f3d1i;
int block;
int h,w;
for (block=0; block <howmanyblocks; block++)
{
for (h=0; h<bh; h++)
{
for (w=0; w<outwidth; w++)
{
// dft 3d (very short - 2 points)
f3d0r = outcur[w][0] + outprev[w][0]; // real 0 (sum)
f3d0i = outcur[w][1] + outprev[w][1]; // im 0 (sum)
f3d1r = outcur[w][0] - outprev[w][0]; // real 1 (dif)
f3d1i = outcur[w][1] - outprev[w][1]; // im 1 (dif)
psd = f3d0r*f3d0r + f3d0i*f3d0i + 1e-15f; // power spectrum density 0
WienerFactor = std::max((psd - pattern3d[w])/psd, lowlimit); // limited Wiener filter
f3d0r *= WienerFactor; // apply filter on real part
f3d0i *= WienerFactor; // apply filter on imaginary part
psd = f3d1r*f3d1r + f3d1i*f3d1i + 1e-15f; // power spectrum density 1
WienerFactor = std::max((psd - pattern3d[w])/psd, lowlimit); // limited Wiener filter
f3d1r *= WienerFactor; // apply filter on real part
f3d1i *= WienerFactor; // apply filter on imaginary part
// reverse dft for 2 points
outprev[w][0] = (f3d0r + f3d1r)*0.5f; // get real part
outprev[w][1] = (f3d0i + f3d1i)*0.5f; // get imaginary part
// Attention! return filtered "out" in "outprev" to preserve "out" for next step
}
outcur += outpitch;
outprev += outpitch;
pattern3d += outpitch;
}
pattern3d -= outpitch*bh; // restore pointer for new block
}
}
//
//-----------------------------------------------------------------------------------------
//
void ApplyWiener3D3_C(fftwf_complex *outcur, fftwf_complex *outprev, fftwf_complex *outnext,
int outwidth, int outpitch, int bh, int howmanyblocks,
float sigmaSquaredNoiseNormed, float beta)
{
// this function take 25% CPU time and may be easy optimized for AMD Athlon 3DNOW assembler
// return result in outprev
float fcr, fci, fpr, fpi, fnr, fni;
float WienerFactor;
float psd;
float lowlimit = (beta-1)/beta; // (beta-1)/beta>=0
float sin120 = 0.86602540378443864676372317075294f;//sqrtf(3.0f)*0.5f;
int block;
int h,w;
for (block=0; block <howmanyblocks; block++)
{
for (h=0; h<bh; h++) // first half
{
for (w=0; w<outwidth; w++) //
{
// dft 3d (very short - 3 points)
float pnr = outprev[w][0] + outnext[w][0];
float pni = outprev[w][1] + outnext[w][1];
fcr = outcur[w][0] + pnr; // real cur
fci = outcur[w][1] + pni; // im cur
float di = sin120*(outprev[w][1]-outnext[w][1]);
float dr = sin120*(outnext[w][0]-outprev[w][0]);
fpr = outcur[w][0] - 0.5f*pnr + di; // real prev
// fnr = outcur[w][0] - 0.5f*pnr - di; // real next
fnr = fpr - di - di; //v1.8.1
fpi = outcur[w][1] - 0.5f*pni + dr; // im prev
// fni = outcur[w][1] - 0.5f*pni - dr ; // im next
fni = fpi - dr - dr; //v1.8.1
psd = fcr*fcr + fci*fci + 1e-15f; // power spectrum density cur
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
fcr *= WienerFactor; // apply filter on real part
fci *= WienerFactor; // apply filter on imaginary part
psd = fpr*fpr + fpi*fpi + 1e-15f; // power spectrum density prev
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
fpr *= WienerFactor; // apply filter on real part
fpi *= WienerFactor; // apply filter on imaginary part
psd = fnr*fnr + fni*fni + 1e-15f; // power spectrum density next
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
fnr *= WienerFactor; // apply filter on real part
fni *= WienerFactor; // apply filter on imaginary part
// reverse dft for 3 points
outprev[w][0] = (fcr + fpr + fnr)*0.33333333333f; // get real part
outprev[w][1] = (fci + fpi + fni)*0.33333333333f; // get imaginary part
// Attention! return filtered "out" in "outprev" to preserve "out" for next step
}
outcur += outpitch;
outprev += outpitch;
outnext += outpitch;
}
}
}
//-----------------------------------------------------------------------------------------
//
void ApplyPattern3D3_C(fftwf_complex *outcur, fftwf_complex *outprev, fftwf_complex *outnext,
int outwidth, int outpitch, int bh, int howmanyblocks,
float *pattern3d, float beta)
{
// this function take 25% CPU time and may be easy optimized for AMD Athlon 3DNOW assembler
// return result in outprev
float fcr, fci, fpr, fpi, fnr, fni;
float WienerFactor;
float psd;
float lowlimit = (beta-1)/beta; // (beta-1)/beta>=0
float sin120 = 0.86602540378443864676372317075294f;//sqrtf(3.0f)*0.5f;
int block;
int h,w;
for (block=0; block <howmanyblocks; block++)
{
for (h=0; h<bh; h++) // first half
{
for (w=0; w<outwidth; w++) //
{
// dft 3d (very short - 3 points)
float pnr = outprev[w][0] + outnext[w][0];
float pni = outprev[w][1] + outnext[w][1];
fcr = outcur[w][0] + pnr; // real cur
fci = outcur[w][1] + pni; // im cur
float di = sin120*(outprev[w][1]-outnext[w][1]);
float dr = sin120*(outnext[w][0]-outprev[w][0]);
fpr = outcur[w][0] - 0.5f*pnr + di; // real prev
fnr = outcur[w][0] - 0.5f*pnr - di; // real next
fpi = outcur[w][1] - 0.5f*pni + dr; // im prev
fni = outcur[w][1] - 0.5f*pni - dr ; // im next
psd = fcr*fcr + fci*fci + 1e-15f; // power spectrum density cur
WienerFactor = std::max((psd - pattern3d[w])/psd, lowlimit); // limited Wiener filter
fcr *= WienerFactor; // apply filter on real part
fci *= WienerFactor; // apply filter on imaginary part
psd = fpr*fpr + fpi*fpi + 1e-15f; // power spectrum density prev
WienerFactor = std::max((psd - pattern3d[w])/psd, lowlimit); // limited Wiener filter
fpr *= WienerFactor; // apply filter on real part
fpi *= WienerFactor; // apply filter on imaginary part
psd = fnr*fnr + fni*fni + 1e-15f; // power spectrum density next
WienerFactor = std::max((psd - pattern3d[w])/psd, lowlimit); // limited Wiener filter
fnr *= WienerFactor; // apply filter on real part
fni *= WienerFactor; // apply filter on imaginary part
// reverse dft for 3 points
outprev[w][0] = (fcr + fpr + fnr)*0.33333333333f; // get real part
outprev[w][1] = (fci + fpi + fni)*0.33333333333f; // get imaginary part
// Attention! return filtered "out" in "outprev" to preserve "out" for next step
}
outcur += outpitch;
outprev += outpitch;
outnext += outpitch;
pattern3d += outpitch;
}
pattern3d -= outpitch*bh; // restore pointer for new block
}
}
//-----------------------------------------------------------------------------------------
//
void ApplyWiener3D4_C(fftwf_complex *outcur, fftwf_complex *outprev2, fftwf_complex *outprev,
fftwf_complex *outnext, int outwidth, int outpitch, int bh,
int howmanyblocks, float sigmaSquaredNoiseNormed, float beta)
{
// dft with 4 points
// this function take 25% CPU time and may be easy optimized for AMD Athlon 3DNOW assembler
// return result in outprev
float fcr, fci, fpr, fpi, fnr, fni, fp2r, fp2i;
float WienerFactor;
float psd;
float lowlimit = (beta-1)/beta; // (beta-1)/beta>=0
int block;
int h,w;
for (block=0; block <howmanyblocks; block++)
{
for (h=0; h<bh; h++) // first half
{
for (w=0; w<outwidth; w++) //
{
// dft 3d (very short - 4 points)
fpr = -outprev2[w][0] + outprev[w][1] + outcur[w][0] - outnext[w][1]; // real prev
fpi = -outprev2[w][1] - outprev[w][0] + outcur[w][1] + outnext[w][0]; // im cur
fcr = outprev2[w][0] + outprev[w][0] + outcur[w][0] + outnext[w][0]; // real cur
fci = outprev2[w][1] + outprev[w][1] + outcur[w][1] + outnext[w][1]; // im cur
fnr = -outprev2[w][0] - outprev[w][1] + outcur[w][0] + outnext[w][1]; // real next
fni = -outprev2[w][1] + outprev[w][0] + outcur[w][1] - outnext[w][0]; // im next
fp2r = outprev2[w][0] - outprev[w][0] + outcur[w][0] - outnext[w][0]; // real prev2
fp2i = outprev2[w][1] - outprev[w][1] + outcur[w][1] - outnext[w][1]; // im cur
psd = fp2r*fp2r + fp2i*fp2i + 1e-15f; // power spectrum density prev2
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
fp2r *= WienerFactor; // apply filter on real part
fp2i *= WienerFactor; // apply filter on imaginary part
psd = fpr*fpr + fpi*fpi + 1e-15f; // power spectrum density prev
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
fpr *= WienerFactor; // apply filter on real part
fpi *= WienerFactor; // apply filter on imaginary part
psd = fcr*fcr + fci*fci + 1e-15f; // power spectrum density cur
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
fcr *= WienerFactor; // apply filter on real part
fci *= WienerFactor; // apply filter on imaginary part
psd = fnr*fnr + fni*fni + 1e-15f; // power spectrum density next
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
fnr *= WienerFactor; // apply filter on real part
fni *= WienerFactor; // apply filter on imaginary part
// reverse dft for 4 points
outprev2[w][0] = (fp2r + fpr + fcr + fnr)*0.25f; // get real part
outprev2[w][1] = (fp2i + fpi + fci + fni)*0.25f; // get imaginary part
// Attention! return filtered "out" in "outprev2" to preserve "out" for next step
}
outcur += outpitch;
outprev2 += outpitch;
outprev += outpitch;
outnext += outpitch;
}
}
}
//-----------------------------------------------------------------------------------------
//
void ApplyPattern3D4_C(fftwf_complex *outcur, fftwf_complex *outprev2, fftwf_complex *outprev,
fftwf_complex *outnext, int outwidth, int outpitch, int bh,
int howmanyblocks, float* pattern3d, float beta)
{
// dft with 4 points
// this function take 25% CPU time and may be easy optimized for AMD Athlon 3DNOW assembler
// return result in outprev
float fcr, fci, fpr, fpi, fnr, fni, fp2r, fp2i;
float WienerFactor;
float psd;
float lowlimit = (beta-1)/beta; // (beta-1)/beta>=0
int block;
int h,w;
for (block=0; block <howmanyblocks; block++)
{
for (h=0; h<bh; h++) // first half
{
for (w=0; w<outwidth; w++) //
{
// dft 3d (very short - 4 points)
fp2r = outprev2[w][0] - outprev[w][0] + outcur[w][0] - outnext[w][0]; // real prev2
fp2i = outprev2[w][1] - outprev[w][1] + outcur[w][1] - outnext[w][1]; // im cur
fpr = -outprev2[w][0] + outprev[w][1] + outcur[w][0] - outnext[w][1]; // real prev
fpi = -outprev2[w][1] - outprev[w][0] + outcur[w][1] + outnext[w][0]; // im cur
fcr = outprev2[w][0] + outprev[w][0] + outcur[w][0] + outnext[w][0]; // real cur
fci = outprev2[w][1] + outprev[w][1] + outcur[w][1] + outnext[w][1]; // im cur
fnr = -outprev2[w][0] - outprev[w][1] + outcur[w][0] + outnext[w][1]; // real next
fni = -outprev2[w][1] + outprev[w][0] + outcur[w][1] - outnext[w][0]; // im next
psd = fp2r*fp2r + fp2i*fp2i + 1e-15f; // power spectrum density prev2
WienerFactor = std::max((psd - pattern3d[w])/psd, lowlimit); // limited Wiener filter
fp2r *= WienerFactor; // apply filter on real part
fp2i *= WienerFactor; // apply filter on imaginary part
psd = fpr*fpr + fpi*fpi + 1e-15f; // power spectrum density prev
WienerFactor = std::max((psd - pattern3d[w])/psd, lowlimit); // limited Wiener filter
fpr *= WienerFactor; // apply filter on real part
fpi *= WienerFactor; // apply filter on imaginary part
psd = fcr*fcr + fci*fci + 1e-15f; // power spectrum density cur
WienerFactor = std::max((psd - pattern3d[w])/psd, lowlimit); // limited Wiener filter
fcr *= WienerFactor; // apply filter on real part
fci *= WienerFactor; // apply filter on imaginary part
psd = fnr*fnr + fni*fni + 1e-15f; // power spectrum density next
WienerFactor = std::max((psd - pattern3d[w])/psd, lowlimit); // limited Wiener filter
fnr *= WienerFactor; // apply filter on real part
fni *= WienerFactor; // apply filter on imaginary part
// reverse dft for 4 points
outprev2[w][0] = (fp2r + fpr + fcr + fnr)*0.25f; // get real part
outprev2[w][1] = (fp2i + fpi + fci + fni)*0.25f; // get imaginary part
// Attention! return filtered "out" in "outprev2" to preserve "out" for next step
}
outcur += outpitch;
outprev2 += outpitch;
outprev += outpitch;
outnext += outpitch;
pattern3d += outpitch;
}
pattern3d -= outpitch*bh; // restore pointer
}
}
//
//-----------------------------------------------------------------------------------------
//
void ApplyKalmanPattern_C( fftwf_complex *outcur, fftwf_complex *outLast,
fftwf_complex *covar, fftwf_complex *covarProcess,
int outwidth, int outpitch, int bh, int howmanyblocks,
float *covarNoiseNormed, float kratio2)
{
// return result in outLast
float GainRe, GainIm; // Kalman Gain
// float filteredRe, filteredIm;
float sumre, sumim;
int block;
int h,w;
for (block=0; block <howmanyblocks; block++)
{
for (h=0; h<bh; h++) //
{
for (w=0; w<outwidth; w++)
{
// use one of possible method for motion detection:
if ( (outcur[w][0]-outLast[w][0])*(outcur[w][0]-outLast[w][0]) > covarNoiseNormed[w]*kratio2 ||
(outcur[w][1]-outLast[w][1])*(outcur[w][1]-outLast[w][1]) > covarNoiseNormed[w]*kratio2 )
{
// big pixel variation due to motion etc
// reset filter
covar[w][0] = covarNoiseNormed[w];
covar[w][1] = covarNoiseNormed[w];
covarProcess[w][0] = covarNoiseNormed[w];
covarProcess[w][1] = covarNoiseNormed[w];
outLast[w][0] = outcur[w][0];
outLast[w][1] = outcur[w][1];
//return result in outLast
}
else
{ // small variation
// useful sum
sumre = (covar[w][0] + covarProcess[w][0]);
sumim = (covar[w][1] + covarProcess[w][1]);
// real gain, imagine gain
GainRe = sumre/(sumre + covarNoiseNormed[w]);
GainIm = sumim/(sumim + covarNoiseNormed[w]);
// update process
covarProcess[w][0] = (GainRe*GainRe*covarNoiseNormed[w]);
covarProcess[w][1] = (GainIm*GainIm*covarNoiseNormed[w]);
// update variation
covar[w][0] = (1-GainRe)*sumre ;
covar[w][1] = (1-GainIm)*sumim ;
outLast[w][0] = ( GainRe*outcur[w][0] + (1 - GainRe)*outLast[w][0] );
outLast[w][1] = ( GainIm*outcur[w][1] + (1 - GainIm)*outLast[w][1] );
//return filtered result in outLast
}
}
outcur += outpitch;
outLast += outpitch;
covar += outpitch;
covarProcess += outpitch;
covarNoiseNormed += outpitch;
}
covarNoiseNormed -= outpitch*bh;
}
}
//-----------------------------------------------------------------------------------------
//
void ApplyKalman_C( fftwf_complex *outcur, fftwf_complex *outLast, fftwf_complex *covar,
fftwf_complex *covarProcess, int outwidth, int outpitch, int bh,
int howmanyblocks, float covarNoiseNormed, float kratio2)
{
// return result in outLast
float GainRe, GainIm; // Kalman Gain
// float filteredRe, filteredIm;
float sumre, sumim;
int block;
int h,w;
float sigmaSquaredMotionNormed = covarNoiseNormed*kratio2;
for (block=0; block <howmanyblocks; block++)
{
for (h=0; h<bh; h++) //
{
for (w=0; w<outwidth; w++)
{
// use one of possible method for motion detection:
if ( (outcur[w][0]-outLast[w][0])*(outcur[w][0]-outLast[w][0]) > sigmaSquaredMotionNormed ||
(outcur[w][1]-outLast[w][1])*(outcur[w][1]-outLast[w][1]) > sigmaSquaredMotionNormed )
{
// big pixel variation due to motion etc
// reset filter
covar[w][0] = covarNoiseNormed;
covar[w][1] = covarNoiseNormed;
covarProcess[w][0] = covarNoiseNormed;
covarProcess[w][1] = covarNoiseNormed;
outLast[w][0] = outcur[w][0];
outLast[w][1] = outcur[w][1];
//return result in outLast
}
else
{ // small variation
// useful sum
sumre = (covar[w][0] + covarProcess[w][0]);
sumim = (covar[w][1] + covarProcess[w][1]);
// real gain, imagine gain
GainRe = sumre/(sumre + covarNoiseNormed);
GainIm = sumim/(sumim + covarNoiseNormed);
// update process
covarProcess[w][0] = (GainRe*GainRe*covarNoiseNormed);
covarProcess[w][1] = (GainIm*GainIm*covarNoiseNormed);
// update variation
covar[w][0] = (1-GainRe)*sumre ;
covar[w][1] = (1-GainIm)*sumim ;
outLast[w][0] = ( GainRe*outcur[w][0] + (1 - GainRe)*outLast[w][0] );
outLast[w][1] = ( GainIm*outcur[w][1] + (1 - GainIm)*outLast[w][1] );
//return filtered result in outLast
}
}
outcur += outpitch;
outLast += outpitch;
covar += outpitch;
covarProcess += outpitch;
}
}
}
//-------------------------------------------------------------------------------------------
//
void Sharpen_C(fftwf_complex *outcur, int outwidth, int outpitch, int bh,
int howmanyblocks, float sharpen, float sigmaSquaredSharpenMin,
float sigmaSquaredSharpenMax, float *wsharpen, float dehalo, float *wdehalo, float ht2n)
{
int h,w, block;
float psd;
float sfact;
if (sharpen != 0 && dehalo==0)
{
for (block =0; block <howmanyblocks; block++)
{
for (h=0; h<bh; h++) // middle
{
for (w=0; w<outwidth; w++)
{
psd = (outcur[w][0]*outcur[w][0] + outcur[w][1]*outcur[w][1]);
//improved sharpen mode to prevent grid artifactes and to limit sharpening both fo low and high amplitudes
sfact = (1 + sharpen*wsharpen[w]*sqrt( psd*sigmaSquaredSharpenMax/((psd + sigmaSquaredSharpenMin)*(psd + sigmaSquaredSharpenMax)) ) ) ;
// sharpen factor - changed in v1.1c
outcur[w][0] *= sfact;
outcur[w][1] *= sfact;
}
outcur += outpitch;
wsharpen += outpitch;
}
wsharpen -= outpitch*bh;
}
}
else if (sharpen == 0 && dehalo != 0)
{
for (block =0; block <howmanyblocks; block++)
{
for (h=0; h<bh; h++) // middle
{
for (w=0; w<outwidth; w++)
{
psd = (outcur[w][0]*outcur[w][0] + outcur[w][1]*outcur[w][1]);
//improved sharpen mode to prevent grid artifactes and to limit sharpening both fo low and high amplitudes
sfact = (psd + ht2n)/((psd + ht2n) + dehalo*wdehalo[w] * psd );
outcur[w][0] *= sfact;
outcur[w][1] *= sfact;
}
outcur += outpitch;
wdehalo += outpitch;
}
wdehalo -= outpitch*bh;
}
}
else if (sharpen != 0 && dehalo != 0)
{
for (block =0; block <howmanyblocks; block++)
{
for (h=0; h<bh; h++) // middle
{
for (w=0; w<outwidth; w++)
{
psd = (outcur[w][0]*outcur[w][0] + outcur[w][1]*outcur[w][1]);
//improved sharpen mode to prevent grid artifactes and to limit sharpening both fo low and high amplitudes
sfact = (1 + sharpen*wsharpen[w]*sqrt( psd*sigmaSquaredSharpenMax/((psd + sigmaSquaredSharpenMin)*(psd + sigmaSquaredSharpenMax)) ) ) *
(psd + ht2n) / ((psd + ht2n) + dehalo*wdehalo[w] * psd );
outcur[w][0] *= sfact;
outcur[w][1] *= sfact;
}
outcur += outpitch;
wsharpen += outpitch;
wdehalo += outpitch;
}
wsharpen -= outpitch*bh;
wdehalo -= outpitch*bh;
}
}
}
//-----------------------------------------------------------------------------------------
//-----------------------------------------------------------------------------------------
// DEGRID
//-----------------------------------------------------------------------------------------
//-------------------------------------------------------------------------------------------
//
void Sharpen_degrid_C(fftwf_complex *outcur, int outwidth, int outpitch, int bh,
int howmanyblocks, float sharpen, float sigmaSquaredSharpenMin,
float sigmaSquaredSharpenMax, float *wsharpen,
float degrid, fftwf_complex *gridsample, float dehalo, float *wdehalo, float ht2n)
{
int h,w, block;
float psd;
float sfact;
if (sharpen != 0 && dehalo==0)
{
for (block =0; block <howmanyblocks; block++)
{
float gridfraction = degrid*outcur[0][0]/gridsample[0][0];
for (h=0; h<bh; h++) // middle
{
for (w=0; w<outwidth; w++)
{
float gridcorrection0 = gridfraction*gridsample[w][0];
float re = outcur[w][0] - gridcorrection0;
float gridcorrection1 = gridfraction*gridsample[w][1];
float im = outcur[w][1] - gridcorrection1;
psd = (re*re + im*im) + 1e-15f;// power spectrum density
// psd = (outcur[w][0]*outcur[w][0] + outcur[w][1]*outcur[w][1]);
//improved sharpen mode to prevent grid artifactes and to limit sharpening both fo low and high amplitudes
sfact = (1 + sharpen*wsharpen[w]*sqrt( psd*sigmaSquaredSharpenMax/((psd + sigmaSquaredSharpenMin)*(psd + sigmaSquaredSharpenMax)) )) ;
// sharpen factor - changed in v1.1c
re *= sfact; // apply filter on real part
im *= sfact; // apply filter on imaginary part
outcur[w][0] = re + gridcorrection0;
outcur[w][1] = im + gridcorrection1;
}
outcur += outpitch;
wsharpen += outpitch;
gridsample += outpitch;
}
wsharpen -= outpitch*bh;
gridsample -= outpitch*bh; // restore pointer to only valid first block - bug fixed in v1.8.1
}
}
if (sharpen == 0 && dehalo != 0)
{
for (block =0; block <howmanyblocks; block++)
{
float gridfraction = degrid*outcur[0][0]/gridsample[0][0];
for (h=0; h<bh; h++) // middle
{
for (w=0; w<outwidth; w++)
{
float gridcorrection0 = gridfraction*gridsample[w][0];
float re = outcur[w][0] - gridcorrection0;
float gridcorrection1 = gridfraction*gridsample[w][1];
float im = outcur[w][1] - gridcorrection1;
psd = (re*re + im*im) + 1e-15f;// power spectrum density
// psd = (outcur[w][0]*outcur[w][0] + outcur[w][1]*outcur[w][1]);
//improved sharpen mode to prevent grid artifactes and to limit sharpening both fo low and high amplitudes
sfact = (psd + ht2n) / ((psd + ht2n) + dehalo*wdehalo[w] * psd );
re *= sfact; // apply filter on real part
im *= sfact; // apply filter on imaginary part
outcur[w][0] = re + gridcorrection0;
outcur[w][1] = im + gridcorrection1;
}
outcur += outpitch;
wsharpen += outpitch;
wdehalo += outpitch;
gridsample += outpitch;
}
wsharpen -= outpitch*bh;
wdehalo -= outpitch*bh;
gridsample -= outpitch*bh; // restore pointer to only valid first block - bug fixed in v1.8.1
}
}
if (sharpen != 0 && dehalo != 0)
{
for (block =0; block <howmanyblocks; block++)
{
float gridfraction = degrid*outcur[0][0]/gridsample[0][0];
for (h=0; h<bh; h++) // middle
{
for (w=0; w<outwidth; w++)
{
float gridcorrection0 = gridfraction*gridsample[w][0];
float re = outcur[w][0] - gridcorrection0;
float gridcorrection1 = gridfraction*gridsample[w][1];
float im = outcur[w][1] - gridcorrection1;
psd = (re*re + im*im) + 1e-15f;// power spectrum density
// psd = (outcur[w][0]*outcur[w][0] + outcur[w][1]*outcur[w][1]);
//improved sharpen mode to prevent grid artifactes and to limit sharpening both fo low and high amplitudes
sfact = (1 + sharpen*wsharpen[w]*sqrt( psd*sigmaSquaredSharpenMax/((psd + sigmaSquaredSharpenMin)*(psd + sigmaSquaredSharpenMax)) )) *
(psd + ht2n)/((psd + ht2n) + dehalo*wdehalo[w] * psd );
re *= sfact; // apply filter on real part
im *= sfact; // apply filter on imaginary part
outcur[w][0] = re + gridcorrection0;
outcur[w][1] = im + gridcorrection1;
}
outcur += outpitch;
wsharpen += outpitch;
wdehalo += outpitch;
gridsample += outpitch;
}
wsharpen -= outpitch*bh;
wdehalo -= outpitch*bh;
gridsample -= outpitch*bh; // restore pointer to only valid first block - bug fixed in v1.8.1
}
}
}
//-----------------------------------------------------------------------------------------
//-----------------------------------------------------------------------------------------
void ApplyWiener2D_degrid_C(fftwf_complex *outcur, int outwidth, int outpitch, int bh,
int howmanyblocks, float sigmaSquaredNoiseNormed, float beta,
float sharpen, float sigmaSquaredSharpenMin,
float sigmaSquaredSharpenMax, float *wsharpen,
float degrid, fftwf_complex *gridsample, float dehalo, float *wdehalo, float ht2n)
{
// this function take 25% CPU time and may be easy optimized for AMD Athlon 3DNOW assembler
float lowlimit = (beta-1)/beta; // (beta-1)/beta>=0
int h,w, block;
float psd;
float WienerFactor;
float gridfraction;
if (sharpen == 0 && dehalo == 0)// no sharpen, no dehalo
{
for (block =0; block <howmanyblocks; block++)
{
gridfraction = degrid*outcur[0][0]/gridsample[0][0];
for (h=0; h<bh; h++) // middle
{
for (w=0; w<outwidth; w++) // not skip first v.1.2
{
float gridcorrection0 = gridfraction*gridsample[w][0];
float corrected0 = outcur[w][0] - gridcorrection0;
float gridcorrection1 = gridfraction*gridsample[w][1];
float corrected1 = outcur[w][1] - gridcorrection1;
psd = (corrected0*corrected0 + corrected1*corrected1 ) + 1e-15f;// power spectrum density
// psd = (outcur[w][0]*outcur[w][0] + outcur[w][1]*outcur[w][1]) + 1e-15f;
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
corrected0 *= WienerFactor; // apply filter on real part
corrected1 *= WienerFactor; // apply filter on imaginary part
outcur[w][0] = corrected0 + gridcorrection0;
outcur[w][1] = corrected1 + gridcorrection1;
}
outcur += outpitch;
gridsample += outpitch;
}
gridsample -= outpitch*bh; // restore pointer to only valid first block
}
}
else if (sharpen != 0 && dehalo==0) // sharpen
{
for (block =0; block <howmanyblocks; block++)
{
gridfraction = degrid*outcur[0][0]/gridsample[0][0];
for (h=0; h<bh; h++) // middle
{
for (w=0; w<outwidth; w++) // not skip first
{
// psd = (outcur[w][0]*outcur[w][0] + outcur[w][1]*outcur[w][1]) + 1e-15f;// power spectrum density
float gridcorrection0 = gridfraction*gridsample[w][0];
float corrected0 = outcur[w][0] - gridcorrection0;
float gridcorrection1 = gridfraction*gridsample[w][1];
float corrected1 = outcur[w][1] - gridcorrection1;
psd = (corrected0*corrected0 + corrected1*corrected1 ) + 1e-15f;// power spectrum density
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
WienerFactor *= 1 + sharpen*wsharpen[w]*sqrt( psd*sigmaSquaredSharpenMax/((psd + sigmaSquaredSharpenMin)*(psd + sigmaSquaredSharpenMax)) ); // sharpen factor - changed in v.1.1
// outcur[w][0] *= WienerFactor; // apply filter on real part
// outcur[w][1] *= WienerFactor; // apply filter on imaginary part
corrected0 *= WienerFactor; // apply filter on real part
corrected1 *= WienerFactor; // apply filter on imaginary part
outcur[w][0] = corrected0 + gridcorrection0;
outcur[w][1] = corrected1 + gridcorrection1;
}
outcur += outpitch;
wsharpen += outpitch;
gridsample += outpitch;
}
wsharpen -= outpitch*bh;
gridsample -= outpitch*bh; // restore pointer to only valid first block
}
}
else if (sharpen == 0 && dehalo != 0)
{
for (block =0; block <howmanyblocks; block++)
{
gridfraction = degrid*outcur[0][0]/gridsample[0][0];
for (h=0; h<bh; h++) // middle
{
for (w=0; w<outwidth; w++) // not skip first
{
// psd = (outcur[w][0]*outcur[w][0] + outcur[w][1]*outcur[w][1]) + 1e-15f;// power spectrum density
float gridcorrection0 = gridfraction*gridsample[w][0];
float corrected0 = outcur[w][0] - gridcorrection0;
float gridcorrection1 = gridfraction*gridsample[w][1];
float corrected1 = outcur[w][1] - gridcorrection1;
psd = (corrected0*corrected0 + corrected1*corrected1 ) + 1e-15f;// power spectrum density
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
WienerFactor *= (psd + ht2n)/((psd + ht2n) + dehalo*wdehalo[w] * psd );
// outcur[w][0] *= WienerFactor; // apply filter on real part
// outcur[w][1] *= WienerFactor; // apply filter on imaginary part
corrected0 *= WienerFactor; // apply filter on real part
corrected1 *= WienerFactor; // apply filter on imaginary part
outcur[w][0] = corrected0 + gridcorrection0;
outcur[w][1] = corrected1 + gridcorrection1;
}
outcur += outpitch;
wdehalo += outpitch;
gridsample += outpitch;
}
wdehalo -= outpitch*bh;
gridsample -= outpitch*bh; // restore pointer to only valid first block
}
}
else if (sharpen != 0 && dehalo != 0)
{
for (block =0; block <howmanyblocks; block++)
{
gridfraction = degrid*outcur[0][0]/gridsample[0][0];
for (h=0; h<bh; h++) // middle
{
for (w=0; w<outwidth; w++) // not skip first
{
// psd = (outcur[w][0]*outcur[w][0] + outcur[w][1]*outcur[w][1]) + 1e-15f;// power spectrum density
float gridcorrection0 = gridfraction*gridsample[w][0];
float corrected0 = outcur[w][0] - gridcorrection0;
float gridcorrection1 = gridfraction*gridsample[w][1];
float corrected1 = outcur[w][1] - gridcorrection1;
psd = (corrected0*corrected0 + corrected1*corrected1 ) + 1e-15f;// power spectrum density
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
// fixed in .10
WienerFactor *= 1 + sharpen * wsharpen[w] * sqrt(psd * sigmaSquaredSharpenMax / ((psd + sigmaSquaredSharpenMin) * (psd + sigmaSquaredSharpenMax)));
WienerFactor *= (psd + ht2n)/((psd + ht2n)+ dehalo*wdehalo[w] * psd );
// outcur[w][0] *= WienerFactor; // apply filter on real part
// outcur[w][1] *= WienerFactor; // apply filter on imaginary part
corrected0 *= WienerFactor; // apply filter on real part
corrected1 *= WienerFactor; // apply filter on imaginary part
outcur[w][0] = corrected0 + gridcorrection0;
outcur[w][1] = corrected1 + gridcorrection1;
}
outcur += outpitch;
wsharpen += outpitch;
gridsample += outpitch;
wdehalo += outpitch;
}
wsharpen -= outpitch*bh;
wdehalo -= outpitch*bh;
gridsample -= outpitch*bh; // restore pointer to only valid first block
}
}
}
//-----------------------------------------------------------------------------------------
//
void ApplyWiener3D2_degrid_C(fftwf_complex *outcur, fftwf_complex *outprev,
int outwidth, int outpitch, int bh, int howmanyblocks,
float sigmaSquaredNoiseNormed, float beta,
float degrid, fftwf_complex *gridsample)
{
// this function take 25% CPU time and may be easy optimized for AMD Athlon 3DNOW assembler
// return result in outprev
float lowlimit = (beta-1)/beta; // (beta-1)/beta>=0
float psd;
float WienerFactor;
float f3d0r, f3d1r, f3d0i, f3d1i;
int block;
int h,w;
for (block=0; block <howmanyblocks; block++)
{
float gridfraction = degrid*outcur[0][0]/gridsample[0][0];
for (h=0; h<bh; h++)
{
for (w=0; w<outwidth; w++)
{
// dft 3d (very short - 2 points)
float gridcorrection0_2 = gridfraction*gridsample[w][0]*2; // grid correction
float gridcorrection1_2 = gridfraction*gridsample[w][1]*2;
f3d0r = outcur[w][0] + outprev[w][0] - gridcorrection0_2; // real 0 (sum)
f3d0i = outcur[w][1] + outprev[w][1] - gridcorrection1_2; // im 0 (sum)
psd = f3d0r*f3d0r + f3d0i*f3d0i + 1e-15f; // power spectrum density 0
WienerFactor = std::max((psd - sigmaSquaredNoiseNormed)/psd, lowlimit); // limited Wiener filter
f3d0r *= WienerFactor; // apply filter on real part
f3d0i *= WienerFactor; // apply filter on imaginary part