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rawTherapee/rtengine/dirpyrLab_denoise.cc

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/*
* This file is part of RawTherapee.
*
* RawTherapee 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 3 of the License, or
* (at your option) any later version.
*
* RawTherapee 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 RawTherapee. If not, see <http://www.gnu.org/licenses/>.
*
* <20> 2010 Emil Martinec <ejmartin@uchicago.edu>
*
*/
#include <cstddef>
#include <cmath>
#include "curves.h"
#include "labimage.h"
#include "improcfun.h"
#include "array2D.h"
#include "rt_math.h"
#ifdef _OPENMP
#include <omp.h>
#endif
#define CLIPC(a) ((a)>-32000?((a)<32000?(a):32000):-32000)
#define DIRWT_L(i1,j1,i,j) ( rangefn_L[(data_fine->L[i1][j1]-data_fine->L[i][j]+32768)] )
#define DIRWT_AB(i1,j1,i,j) ( rangefn_ab[(data_fine->a[i1][j1]-data_fine->a[i][j]+32768)] * \
rangefn_ab[(data_fine->L[i1][j1]-data_fine->L[i][j]+32768)] * \
rangefn_ab[(data_fine->b[i1][j1]-data_fine->b[i][j]+32768)] )
//#define NRWT_L(a) (nrwt_l[a] )
#define NRWT_AB (nrwt_ab[(hipass[1]+32768)] * nrwt_ab[(hipass[2]+32768)])
#define med3(a,b,c) (a<b ? (b<c ? b : (a<c ? c : a)) : (a<c ? a : (b<c ? c : b)))
#define hmf(a11,a12,a13,a21,a22,a23,a31,a32,a33) \
(med3(a22,med3(a22,med3(a12,a22,a32),med3(a21,a22,a23)), \
med3(a22,med3(a11,a22,a33),med3(a13,a22,a31))) )
#define PIX_SORT(a,b) { if ((a)>(b)) {temp=(a);(a)=(b);(b)=temp;} }
#define med3x3(a0,a1,a2,a3,a4,a5,a6,a7,a8,median) { \
p[0]=a0; p[1]=a1; p[2]=a2; p[3]=a3; p[4]=a4; p[5]=a5; p[6]=a6; p[7]=a7; p[8]=a8; \
PIX_SORT(p[1],p[2]); PIX_SORT(p[4],p[5]); PIX_SORT(p[7],p[8]); \
PIX_SORT(p[0],p[1]); PIX_SORT(p[3],p[4]); PIX_SORT(p[6],p[7]); \
PIX_SORT(p[1],p[2]); PIX_SORT(p[4],p[5]); PIX_SORT(p[7],p[8]); \
PIX_SORT(p[0],p[3]); PIX_SORT(p[5],p[8]); PIX_SORT(p[4],p[7]); \
PIX_SORT(p[3],p[6]); PIX_SORT(p[1],p[4]); PIX_SORT(p[2],p[5]); \
PIX_SORT(p[4],p[7]); PIX_SORT(p[4],p[2]); PIX_SORT(p[6],p[4]); \
PIX_SORT(p[4],p[2]); median=p[4];} //a4 is the median
namespace rtengine
{
static const int maxlevel = 4;
//sequence of scales
//static const int scales[8] = {1,2,4,8,16,32,64,128};
//sequence of pitches
//static const int pitches[8] = {1,1,1,1,1,1,1,1};
//sequence of scales
//static const int scales[8] = {1,1,1,1,1,1,1,1};
//sequence of pitches
//static const int pitches[8] = {2,2,2,2,2,2,2,2};
//sequence of scales
//static const int scales[8] = {1,1,2,2,4,4,8,8};
//sequence of pitches
//static const int pitches[8] = {2,1,2,1,2,1,2,1};
//sequence of scales
static const int scales[8] = {1, 1, 2, 4, 8, 16, 32, 64};
//sequence of pitches
static const int pitches[8] = {2, 1, 1, 1, 1, 1, 1, 1};
//pitch is spacing of subsampling
//scale is spacing of directional averaging weights
//example 1: no subsampling at any level -- pitch=1, scale=2^n
//example 2: subsampling by 2 every level -- pitch=2, scale=1 at each level
//example 3: no subsampling at first level, subsampling by 2 thereafter --
// pitch =1, scale=1 at first level; pitch=2, scale=2 thereafter
void ImProcFunctions :: dirpyrLab_denoise(LabImage * src, LabImage * dst, const procparams::DirPyrDenoiseParams & dnparams )
{
float gam = dnparams.gamma / 3.0;
//float gam = 2.0;//min(3.0, 0.1*fabs(c[4])/3.0+0.001);
float gamthresh = 0.03;
float gamslope = exp(log((double)gamthresh) / gam) / gamthresh;
LUTf gamcurve(65536, 0);
//DiagonalCurve* lumacurve = new DiagonalCurve (dnparams.lumcurve, CURVES_MIN_POLY_POINTS);
//DiagonalCurve* chromacurve = new DiagonalCurve (dnparams.chromcurve, CURVES_MIN_POLY_POINTS);
//LUTf Lcurve(65536);
//LUTf abcurve(65536);
for (int i = 0; i < 65536; i++) {
int g = (int)(CurveFactory::gamma((double)i / 65535.0, gam, gamthresh, gamslope, 1.0, 0.0) * 65535.0);
gamcurve[i] = CLIP(g);
/*float val = (float)i/65535.0;
float Lval = (2*(lumacurve->getVal(val)));
float abval = (2*(chromacurve->getVal(val)));
Lcurve[i] = SQR(Lval);
abcurve[i] = SQR(abval);
if (i % 1000 ==0) printf("%d Lmult=%f abmult=%f \n",i,Lcurve[i],abcurve[i]);*/
}
//delete lumacurve;
//delete chromacurve;
//#pragma omp parallel for if (multiThread)
for (int i = 0; i < src->H; i++) {
for (int j = 0; j < src->W; j++) {
//src->L[i][j] = CurveFactory::flinterp(gamcurve,src->L[i][j]);
src->L[i][j] = gamcurve[src->L[i][j]];
}
}
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
LUTf rangefn_L(65536);
LUTf nrwt_l(65536);
LUTf rangefn_ab(65536);
LUTf nrwt_ab(65536);
//set up NR weight functions
//gamma correction for chroma in shadows
float nrwtl_norm = ((CurveFactory::gamma((double)65535.0 / 65535.0, gam, gamthresh, gamslope, 1.0, 0.0)) -
(CurveFactory::gamma((double)75535.0 / 65535.0, gam, gamthresh, gamslope, 1.0, 0.0)));
for (int i = 0; i < 65536; i++) {
nrwt_l[i] = ((CurveFactory::gamma((double)i / 65535.0, gam, gamthresh, gamslope, 1.0, 0.0) -
CurveFactory::gamma((double)(i + 10000) / 65535.0, gam, gamthresh, gamslope, 1.0, 0.0)) ) / nrwtl_norm;
//if (i % 100 ==0) printf("%d %f \n",i,nrwt_l[i]);
}
float tonefactor = nrwt_l[32768];
float noise_L = 10.0 * dnparams.luma;
float noisevar_L = SQR(noise_L);
float noise_ab = 100.0 * dnparams.chroma;
float noisevar_ab = SQR(noise_ab);
//set up range functions
for (int i = 0; i < 65536; i++) {
rangefn_L[i] = (( exp(-(double)fabs(i - 32768) * tonefactor / (1.0 + noise_L)) * (1.0 + noisevar_L) / ((double)(i - 32768) * (double)(i - 32768) + noisevar_L + 1.0)));
}
for (int i = 0; i < 65536; i++) {
rangefn_ab[i] = (( exp(-(double)fabs(i - 32768) * tonefactor / (1.0 + 3 * noise_ab)) * (1.0 + noisevar_ab) / ((double)(i - 32768) * (double)(i - 32768) + noisevar_ab + 1.0)));
}
for (int i = 0; i < 65536; i++) {
nrwt_ab[i] = ((1.0 + abs(i - 32768) / (1.0 + 8 * noise_ab)) * exp(-(double)fabs(i - 32768) / (1.0 + 8 * noise_ab) ) );
}
//for (int i=0; i<65536; i+=100) printf("%d %d \n",i,gamcurve[i]);
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
int level;
LabImage * dirpyrLablo[maxlevel];
int w = (int)((src->W - 1) / pitches[0]) + 1;
int h = (int)((src->H - 1) / pitches[0]) + 1;
dirpyrLablo[0] = new LabImage(w, h);
for (level = 1; level < maxlevel; level++) {
w = (int)((w - 1) / pitches[level]) + 1;
h = (int)((h - 1) / pitches[level]) + 1;
dirpyrLablo[level] = new LabImage(w, h);
};
//////////////////////////////////////////////////////////////////////////////
// c[0] = luma = noise_L
// c[1] = chroma = noise_ab
// c[2] decrease of noise var with scale
// c[3] radius of domain blur at each level
// c[4] shadow smoothing
// c[5] edge preservation
level = 0;
int scale = scales[level];
int pitch = pitches[level];
//int thresh = 10 * c[8];
//impulse_nr (src, src, m_w1, m_h1, thresh, noisevar);
dirpyr(src, dirpyrLablo[0], 0, rangefn_L, rangefn_ab, pitch, scale, dnparams.luma, dnparams.chroma );
level = 1;
while(level < maxlevel) {
scale = scales[level];
pitch = pitches[level];
dirpyr(dirpyrLablo[level - 1], dirpyrLablo[level], level, rangefn_L, rangefn_ab, pitch, scale, dnparams.luma, dnparams.chroma );
level ++;
}
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for(int level = maxlevel - 1; level > 0; level--) {
int scale = scales[level];
int pitch = pitches[level];
idirpyr(dirpyrLablo[level], dirpyrLablo[level - 1], level, rangefn_L, nrwt_l, nrwt_ab, pitch, scale, dnparams.luma, dnparams.chroma/*, Lcurve, abcurve*/ );
}
scale = scales[0];
pitch = pitches[0];
// freeing as much memory as possible since the next call to idirpyr will need lots
for(int i = 1; i < maxlevel; i++) {
delete dirpyrLablo[i];
}
idirpyr(dirpyrLablo[0], dst, 0, rangefn_L, nrwt_l, nrwt_ab, pitch, scale, dnparams.luma, dnparams.chroma/*, Lcurve, abcurve*/ );
// freeing the last bunch of memory
delete dirpyrLablo[0];
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
float igam = 1 / gam;
float igamthresh = gamthresh * gamslope;
float igamslope = 1 / gamslope;
for (int i = 0; i < 65536; i++) {
gamcurve[i] = (CurveFactory::gamma((float)i / 65535.0, igam, igamthresh, igamslope, 1.0, 0.0) * 65535.0);
}
if (dnparams.luma > 0) {
for (int i = 0; i < dst->H; i++)
for (int j = 0; j < dst->W; j++) {
dst->L[i][j] = gamcurve[dst->L[i][j]];
}
} else {
for (int i = 0; i < dst->H; i++)
for (int j = 0; j < dst->W; j++) {
dst->L[i][j] = gamcurve[src->L[i][j]];
}
}
}
void ImProcFunctions::dirpyr(LabImage* data_fine, LabImage* data_coarse, int level,
LUTf & rangefn_L, LUTf & rangefn_ab, int pitch, int scale,
const int luma, const int chroma )
{
//pitch is spacing of subsampling
//scale is spacing of directional averaging weights
//example 1: no subsampling at any level -- pitch=1, scale=2^n
//example 2: subsampling by 2 every level -- pitch=2, scale=1 at each level
//example 3: no subsampling at first level, subsampling by 2 thereafter --
// pitch =1, scale=1 at first level; pitch=2, scale=2 thereafter
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
// calculate weights, compute directionally weighted average
int width = data_fine->W;
int height = data_fine->H;
//generate domain kernel
int halfwin = 3;//min(ceil(2*sig),3);
int scalewin = halfwin * scale;
#ifdef _OPENMP
#pragma omp parallel for
#endif
for(int i = 0; i < height; i += pitch ) {
int i1 = i / pitch;
for(int j = 0, j1 = 0; j < width; j += pitch, j1++) {
float dirwt_l, dirwt_ab, norm_l, norm_ab;
//float lops,aops,bops;
float Lout, aout, bout;
norm_l = norm_ab = 0;//if we do want to include the input pixel in the sum
Lout = 0;
aout = 0;
bout = 0;
for(int inbr = (i - scalewin); inbr <= (i + scalewin); inbr += scale) {
if (inbr < 0 || inbr > height - 1) {
continue;
}
for (int jnbr = (j - scalewin); jnbr <= (j + scalewin); jnbr += scale) {
if (jnbr < 0 || jnbr > width - 1) {
continue;
}
dirwt_l = DIRWT_L(inbr, jnbr, i, j);
dirwt_ab = DIRWT_AB(inbr, jnbr, i, j);
Lout += dirwt_l * data_fine->L[inbr][jnbr];
aout += dirwt_ab * data_fine->a[inbr][jnbr];
bout += dirwt_ab * data_fine->b[inbr][jnbr];
norm_l += dirwt_l;
norm_ab += dirwt_ab;
}
}
//lops = Lout/norm;//diagnostic
//aops = aout/normab;//diagnostic
//bops = bout/normab;//diagnostic
data_coarse->L[i1][j1] = Lout / norm_l; //low pass filter
data_coarse->a[i1][j1] = aout / norm_ab;
data_coarse->b[i1][j1] = bout / norm_ab;
/*if (level<2 && i>0 && i<height-1 && j>0 && j<width-1) {
Lhmf = hmf(data_fine->L[i-1][j-1], data_fine->L[i-1][j], data_fine->L[i-1][j+1], \
data_fine->L[i][j-1], data_fine->L[i][j], data_fine->L[i][j+1], \
data_fine->L[i+1][j-1], data_fine->L[i+1][j], data_fine->L[i+1][j+1]);
//med3x3(data_fine->L[i-1][j-1], data_fine->L[i-1][j], data_fine->L[i-1][j+1], \
data_fine->L[i][j-1], data_fine->L[i][j], data_fine->L[i][j+1], \
data_fine->L[i+1][j-1], data_fine->L[i+1][j], data_fine->L[i+1][j+1],Lmed);
data_coarse->L[i1][j1] = Lhmf;
}*/
}
}
}
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
void ImProcFunctions::idirpyr(LabImage* data_coarse, LabImage* data_fine, int level, LUTf &rangefn_L, LUTf & nrwt_l, LUTf & nrwt_ab,
int pitch, int scale, const int luma, const int chroma/*, LUTf & Lcurve, LUTf & abcurve*/ )
{
int width = data_fine->W;
int height = data_fine->H;
array2D<float> nrfactorL (width, height);
//float eps = 0.0;
// c[0] noise_L
// c[1] noise_ab (relative to noise_L)
// c[2] decrease of noise var with scale
// c[3] radius of domain blur at each level
// c[4] shadow smoothing
float noisevar_L = 4 * SQR(25.0 * luma);
float noisevar_ab = 2 * SQR(100.0 * chroma);
float scalefactor = 1.0 / pow(2.0, (level + 1) * 2); //change the last 2 to 1 for longer tail of higher scale NR
noisevar_L *= scalefactor;
// for coarsest level, take non-subsampled lopass image and subtract from lopass_fine to generate hipass image
// denoise hipass image, add back into lopass_fine to generate denoised image at fine scale
// now iterate:
// (1) take denoised image at level n, expand and smooth using gradient weights from lopass image at level n-1
// the result is the smoothed image at level n-1
// (2) subtract smoothed image at level n-1 from lopass image at level n-1 to make hipass image at level n-1
// (3) denoise the hipass image at level n-1
// (4) add the denoised image at level n-1 to the smoothed image at level n-1 to make the denoised image at level n-1
// note that the coarsest level amounts to skipping step (1) and doing (2,3,4).
// in other words, skip step one if pitch=1
// step (1)
if (pitch == 1) {
// step (1-2-3-4)
#ifdef _OPENMP
#pragma omp parallel
#endif
{
#ifdef _OPENMP
#pragma omp for
#endif
for(int i = 0; i < height; i++)
for(int j = 0; j < width; j++)
{
float hipass[3], hpffluct[3], tonefactor, nrfactor;
tonefactor = (nrwt_l[data_coarse->L[i][j]]);
hipass[1] = data_fine->a[i][j] - data_coarse->a[i][j];
hipass[2] = data_fine->b[i][j] - data_coarse->b[i][j];
//Wiener filter
//luma
if (level < 2) {
hipass[0] = data_fine->L[i][j] - data_coarse->L[i][j];
hpffluct[0] = SQR(hipass[0]) + SQR(hipass[1]) + SQR(hipass[2]) + 0.001;
nrfactorL[i][j] = (1.0 + hpffluct[0]) / (1.0 + hpffluct[0] + noisevar_L /* * Lcurve[data_coarse->L[i][j]]*/);
//hipass[0] *= hpffluct[0]/(hpffluct[0]+noisevar_L);
//data_fine->L[i][j] = CLIP(hipass[0]+data_coarse->L[i][j]);
}
//chroma
//hipass[1] = data_fine->a[i][j]-data_coarse->a[i][j];
//hipass[2] = data_fine->b[i][j]-data_coarse->b[i][j];
hpffluct[1] = SQR(hipass[1] * tonefactor) + 0.001;
hpffluct[2] = SQR(hipass[2] * tonefactor) + 0.001;
nrfactor = (hpffluct[1] + hpffluct[2]) / ((hpffluct[1] + hpffluct[2]) + noisevar_ab * NRWT_AB);
hipass[1] *= nrfactor;
hipass[2] *= nrfactor;
data_fine->a[i][j] = hipass[1] + data_coarse->a[i][j];
data_fine->b[i][j] = hipass[2] + data_coarse->b[i][j];
}
if (level < 2)
{
#ifdef _OPENMP
#pragma omp for
#endif
for(int i = 0; i < height; i++)
for(int j = 0; j < width; j++) {
float dirwt_l, norm_l;
float nrfctrave = 0;
norm_l = 0;//if we do want to include the input pixel in the sum
for(int inbr = max(0, i - 1); inbr <= min(height - 1, i + 1); inbr++) {
for (int jnbr = max(0, j - 1); jnbr <= min(width - 1, j + 1); jnbr++) {
dirwt_l = DIRWT_L(inbr, jnbr, i, j);
nrfctrave += dirwt_l * nrfactorL[inbr][jnbr];
norm_l += dirwt_l;
}
}
nrfctrave /= norm_l;
//nrfctrave = nrfactorL[i][j];
//nrfctrave=1;
float hipass[3];
//luma
/*if (i>0 && i<height-1 && j>0 && j<width-1) {
med3x3(nrfactorL[i-1][j-1], nrfactorL[i-1][j], nrfactorL[i-1][j+1], \
nrfactorL[i][j-1], nrfactorL[i][j], nrfactorL[i][j+1], \
nrfactorL[i+1][j-1], nrfactorL[i+1][j], nrfactorL[i+1][j+1], median);
//median = hmf(nrfactorL[i-1][j-1], nrfactorL[i-1][j], nrfactorL[i-1][j+1], \
nrfactorL[i][j-1], nrfactorL[i][j], nrfactorL[i][j+1], \
nrfactorL[i+1][j-1], nrfactorL[i+1][j], nrfactorL[i+1][j+1]);
//median = nrfactorL[i][j];
} else {
median = nrfactorL[i][j];
}*/
hipass[0] = nrfctrave * (data_fine->L[i][j] - data_coarse->L[i][j]);
//hipass[0] = median*(data_fine->L[i][j]-data_coarse->L[i][j]);
//hipass[0] = nrfactorL[i][j]*(data_fine->L[i][j]-data_coarse->L[i][j]);
data_fine->L[i][j] = CLIP(hipass[0] + data_coarse->L[i][j]);
//chroma
//hipass[1] = nrfactorab[i][j]*(data_fine->a[i][j]-data_coarse->a[i][j]);
//hipass[2] = nrfactorab[i][j]*(data_fine->b[i][j]-data_coarse->b[i][j]);
//data_fine->a[i][j] = hipass[1]+data_coarse->a[i][j];
//data_fine->b[i][j] = hipass[2]+data_coarse->b[i][j];
}
}//end of luminance correction
}//end of pitch=1
} else {//pitch>1
LabImage* smooth;
smooth = new LabImage(width, height);
#ifdef _OPENMP
#pragma omp parallel
#endif
{
#ifdef _OPENMP
#pragma omp for
#endif
for(int i = 0; i < height; i += pitch) {
int ix = i / pitch;
for(int j = 0, jx = 0; j < width; j += pitch, jx++) {
//copy common pixels
smooth->L[i][j] = data_coarse->L[ix][jx];
smooth->a[i][j] = data_coarse->a[ix][jx];
smooth->b[i][j] = data_coarse->b[ix][jx];
}
}
//if (pitch>1) {//pitch=2; step (1) expand coarse image, fill in missing data
#ifdef _OPENMP
#pragma omp for
#endif
for(int i = 0; i < height - 1; i += 2)
for(int j = 0; j < width - 1; j += 2) {
//do midpoint first
double norm = 0.0, wtdsum[3] = {0.0, 0.0, 0.0};
//wtdsum[0]=wtdsum[1]=wtdsum[2]=0.0;
for(int ix = i; ix < min(height, i + 3); ix += 2)
for (int jx = j; jx < min(width, j + 3); jx += 2) {
wtdsum[0] += smooth->L[ix][jx];
wtdsum[1] += smooth->a[ix][jx];
wtdsum[2] += smooth->b[ix][jx];
norm++;
}
norm = 1 / norm;
smooth->L[i + 1][j + 1] = wtdsum[0] * norm;
smooth->a[i + 1][j + 1] = wtdsum[1] * norm;
smooth->b[i + 1][j + 1] = wtdsum[2] * norm;
}
#ifdef _OPENMP
#pragma omp for
#endif
for(int i = 0; i < height - 1; i += 2)
for(int j = 0; j < width - 1; j += 2) {
//now right neighbor
if (j + 1 == width) {
continue;
}
double norm = 0.0, wtdsum[3] = {0.0, 0.0, 0.0};
for (int jx = j; jx < min(width, j + 3); jx += 2) {
wtdsum[0] += smooth->L[i][jx];
wtdsum[1] += smooth->a[i][jx];
wtdsum[2] += smooth->b[i][jx];
norm++;
}
for (int ix = max(0, i - 1); ix < min(height, i + 2); ix += 2) {
wtdsum[0] += smooth->L[ix][j + 1];
wtdsum[1] += smooth->a[ix][j + 1];
wtdsum[2] += smooth->b[ix][j + 1];
norm++;
}
norm = 1 / norm;
smooth->L[i][j + 1] = wtdsum[0] * norm;
smooth->a[i][j + 1] = wtdsum[1] * norm;
smooth->b[i][j + 1] = wtdsum[2] * norm;
//now down neighbor
if (i + 1 == height) {
continue;
}
norm = 0.0;
wtdsum[0] = wtdsum[1] = wtdsum[2] = 0.0;
for (int ix = i; ix < min(height, i + 3); ix += 2) {
wtdsum[0] += smooth->L[ix][j];
wtdsum[1] += smooth->a[ix][j];
wtdsum[2] += smooth->b[ix][j];
norm++;
}
for (int jx = max(0, j - 1); jx < min(width, j + 2); jx += 2) {
wtdsum[0] += smooth->L[i + 1][jx];
wtdsum[1] += smooth->a[i + 1][jx];
wtdsum[2] += smooth->b[i + 1][jx];
norm++;
}
norm = 1 / norm;
smooth->L[i + 1][j] = wtdsum[0] * norm;
smooth->a[i + 1][j] = wtdsum[1] * norm;
smooth->b[i + 1][j] = wtdsum[2] * norm;
}
#ifdef _OPENMP
#pragma omp for
#endif
// step (2-3-4)
for( int i = 0; i < height; i++)
for(int j = 0; j < width; j++) {
float tonefactor = (nrwt_l[smooth->L[i][j]]);
//double wtdsum[3], norm;
float hipass[3], hpffluct[3], nrfactor;
hipass[1] = data_fine->a[i][j] - smooth->a[i][j];
hipass[2] = data_fine->b[i][j] - smooth->b[i][j];
//Wiener filter
//luma
if (level < 2) {
hipass[0] = data_fine->L[i][j] - smooth->L[i][j];
hpffluct[0] = SQR(hipass[0]) + SQR(hipass[1]) + SQR(hipass[2]) + 0.001;
nrfactorL[i][j] = (1.0 + hpffluct[0]) / (1.0 + hpffluct[0] + noisevar_L /* * Lcurve[smooth->L[i][j]]*/);
//hipass[0] *= hpffluct[0]/(hpffluct[0]+noisevar_L);
//data_fine->L[i][j] = CLIP(hipass[0]+smooth->L[i][j]);
}
//chroma
//hipass[1] = data_fine->a[i][j]-smooth->a[i][j];
//hipass[2] = data_fine->b[i][j]-smooth->b[i][j];
hpffluct[1] = SQR(hipass[1] * tonefactor) + 0.001;
hpffluct[2] = SQR(hipass[2] * tonefactor) + 0.001;
nrfactor = (hpffluct[1] + hpffluct[2]) / ((hpffluct[1] + hpffluct[2]) + noisevar_ab * NRWT_AB /* * abcurve[smooth->L[i][j]]*/);
hipass[1] *= nrfactor;
hipass[2] *= nrfactor;
data_fine->a[i][j] = hipass[1] + smooth->a[i][j];
data_fine->b[i][j] = hipass[2] + smooth->b[i][j];
}
if (level < 2) {
#ifdef _OPENMP
#pragma omp for
#endif
for(int i = 0; i < height; i++)
for(int j = 0; j < width; j++) {
float dirwt_l, norm_l;
float nrfctrave = 0;
norm_l = 0;//if we do want to include the input pixel in the sum
for(int inbr = (i - pitch); inbr <= (i + pitch); inbr += pitch) {
if (inbr < 0 || inbr > height - 1) {
continue;
}
for (int jnbr = (j - pitch); jnbr <= (j + pitch); jnbr += pitch) {
if (jnbr < 0 || jnbr > width - 1) {
continue;
}
dirwt_l = DIRWT_L(inbr, jnbr, i, j);
nrfctrave += dirwt_l * nrfactorL[inbr][jnbr];
norm_l += dirwt_l;
}
}
nrfctrave /= norm_l;
//nrfctrave = nrfactorL[i][j];
//nrfctrave=1;
float hipass[3];
//luma
/*if (i>0 && i<height-1 && j>0 && j<width-1) {
//med3x3(nrfactorL[i-1][j-1], nrfactorL[i-1][j], nrfactorL[i-1][j+1], \
nrfactorL[i][j-1], nrfactorL[i][j], nrfactorL[i][j+1], \
nrfactorL[i+1][j-1], nrfactorL[i+1][j], nrfactorL[i+1][j+1], median);
median = hmf(nrfactorL[i-1][j-1], nrfactorL[i-1][j], nrfactorL[i-1][j+1], \
nrfactorL[i][j-1], nrfactorL[i][j], nrfactorL[i][j+1], \
nrfactorL[i+1][j-1], nrfactorL[i+1][j], nrfactorL[i+1][j+1]);
} else {
median = nrfactorL[i][j];
}*/
hipass[0] = nrfctrave * (data_fine->L[i][j] - smooth->L[i][j]);
//hipass[0] = median*(data_fine->L[i][j]-smooth->L[i][j]);
//hipass[0] = nrfactorL[i][j]*(data_fine->L[i][j]-data_coarse->L[i][j]);
data_fine->L[i][j] = CLIP(hipass[0] + smooth->L[i][j]);
//chroma
//hipass[1] = nrfactorab[i][j]*(data_fine->a[i][j]-data_coarse->a[i][j]);
//hipass[2] = nrfactorab[i][j]*(data_fine->b[i][j]-data_coarse->b[i][j]);
//data_fine->a[i][j] = hipass[1]+data_coarse->a[i][j];
//data_fine->b[i][j] = hipass[2]+data_coarse->b[i][j];
}
}//end of luminance correction
} // end parallel
delete smooth;
}//end of pitch>1
}
#undef DIRWT_L
#undef DIRWT_AB
//#undef NRWT_L
#undef NRWT_AB
}
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