271 lines
20 KiB
C++
271 lines
20 KiB
C++
/*
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* This file is part of RawTherapee.
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*
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* Copyright (c) 2017-2018 Luis Sanz Rodriguez (luis.sanz.rodriguez(at)gmail(dot)com) and Ingo Weyrich (heckflosse67@gmx.de)
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*
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* RawTherapee is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* RawTherapee is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with RawTherapee. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include <cmath>
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#include "rawimagesource.h"
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#include "rt_math.h"
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#include "../rtgui/multilangmgr.h"
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#include "opthelper.h"
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#define BENCHMARK
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#include "StopWatch.h"
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using namespace std;
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namespace rtengine
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{
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/**
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* RATIO CORRECTED DEMOSAICING
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* Luis Sanz Rodriguez (luis.sanz.rodriguez(at)gmail(dot)com)
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*
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* Release 2.3 @ 171125
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*
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* Original code from https://github.com/LuisSR/RCD-Demosaicing
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* Licensed under the GNU GPL version 3
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*/
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// Tiled version by Ingo Weyrich (heckflosse67@gmx.de)
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void RawImageSource::rcd_demosaic()
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{
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BENCHFUN
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if (plistener) {
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plistener->setProgressStr(Glib::ustring::compose(M("TP_RAW_DMETHOD_PROGRESSBAR"), "rcd"));
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plistener->setProgress(0);
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}
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const int width = W, height = H;
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constexpr int tileBorder = 8;
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constexpr int tileSize = 228;
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constexpr int tileSizeN = tileSize - 2 * tileBorder;
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const int numTh = H / (tileSizeN) + ((H % (tileSizeN)) ? 1 : 0);
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const int numTw = W / (tileSizeN) + ((W % (tileSizeN)) ? 1 : 0);
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constexpr int w1 = tileSize, w2 = 2 * tileSize, w3 = 3 * tileSize, w4 = 4 * tileSize;
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//Tolerance to avoid dividing by zero
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static constexpr float eps = 1e-5f;
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static constexpr float epssq = 1e-10f;
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#ifdef _OPENMP
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#pragma omp parallel
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#endif
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{
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float *cfa = (float*) calloc( tileSize * tileSize, sizeof *cfa );
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float (*rgb)[tileSize * tileSize] = (float (*)[tileSize * tileSize])malloc(3 * sizeof *rgb);
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float *VH_Dir = (float*) calloc( tileSize * tileSize, sizeof *VH_Dir );
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float *PQ_Dir = (float*) calloc( tileSize * tileSize, sizeof *PQ_Dir );
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float *lpf = PQ_Dir; // reuse buffer, they don't overlap in usage
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#ifdef _OPENMP
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#pragma omp for schedule(dynamic) collapse(2) nowait
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#endif
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for(int tr = 0; tr < numTh; ++tr) {
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for(int tc = 0; tc < numTw; ++tc) {
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int rowStart = tr * tileSizeN;
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int rowEnd = std::min(tr * tileSizeN + tileSize, H);
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int colStart = tc * tileSizeN;
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int colEnd = std::min(tc * tileSizeN + tileSize, W);
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for (int row = rowStart; row < rowEnd; row++) {
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int indx = (row - rowStart) * tileSize;
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int c0 = FC(row, colStart);
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int c1 = FC(row, colStart + 1);
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int col = colStart;
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for (; col < colEnd - 1; col+=2, indx+=2) {
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cfa[indx] = rgb[c0][indx] = LIM01(rawData[row][col] / 65535.f);
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cfa[indx + 1] = rgb[c1][indx + 1] = LIM01(rawData[row][col + 1] / 65535.f);
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}
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if(col < colEnd) {
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cfa[indx] = rgb[c0][indx] = LIM01(rawData[row][col] / 65535.f);
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}
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}
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/**
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* STEP 1: Find cardinal and diagonal interpolation directions
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*/
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for (int row = 4; row < tileSize - 4; row++ ) {
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for (int col = 4, indx = row * tileSize + col; col < tileSize - 4; col++, indx++ ) {
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//Calculate h/v local discrimination
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float V_Stat = max(epssq, - 18.0f * cfa[indx] * cfa[indx - w1] - 18.0f * cfa[indx] * cfa[indx + w1] - 36.0f * cfa[indx] * cfa[indx - w2] - 36.0f * cfa[indx] * cfa[indx + w2] + 18.0f * cfa[indx] * cfa[indx - w3] + 18.0f * cfa[indx] * cfa[indx + w3] - 2.0f * cfa[indx] * cfa[indx - w4] - 2.0f * cfa[indx] * cfa[indx + w4] + 38.0f * cfa[indx] * cfa[indx] - 70.0f * cfa[indx - w1] * cfa[indx + w1] - 12.0f * cfa[indx - w1] * cfa[indx - w2] + 24.0f * cfa[indx - w1] * cfa[indx + w2] - 38.0f * cfa[indx - w1] * cfa[indx - w3] + 16.0f * cfa[indx - w1] * cfa[indx + w3] + 12.0f * cfa[indx - w1] * cfa[indx - w4] - 6.0f * cfa[indx - w1] * cfa[indx + w4] + 46.0f * cfa[indx - w1] * cfa[indx - w1] + 24.0f * cfa[indx + w1] * cfa[indx - w2] - 12.0f * cfa[indx + w1] * cfa[indx + w2] + 16.0f * cfa[indx + w1] * cfa[indx - w3] - 38.0f * cfa[indx + w1] * cfa[indx + w3] - 6.0f * cfa[indx + w1] * cfa[indx - w4] + 12.0f * cfa[indx + w1] * cfa[indx + w4] + 46.0f * cfa[indx + w1] * cfa[indx + w1] + 14.0f * cfa[indx - w2] * cfa[indx + w2] - 12.0f * cfa[indx - w2] * cfa[indx + w3] - 2.0f * cfa[indx - w2] * cfa[indx - w4] + 2.0f * cfa[indx - w2] * cfa[indx + w4] + 11.0f * cfa[indx - w2] * cfa[indx - w2] - 12.0f * cfa[indx + w2] * cfa[indx - w3] + 2.0f * cfa[indx + w2] * cfa[indx - w4] - 2.0f * cfa[indx + w2] * cfa[indx + w4] + 11.0f * cfa[indx + w2] * cfa[indx + w2] + 2.0f * cfa[indx - w3] * cfa[indx + w3] - 6.0f * cfa[indx - w3] * cfa[indx - w4] + 10.0f * cfa[indx - w3] * cfa[indx - w3] - 6.0f * cfa[indx + w3] * cfa[indx + w4] + 10.0f * cfa[indx + w3] * cfa[indx + w3] + 1.0f * cfa[indx - w4] * cfa[indx - w4] + 1.0f * cfa[indx + w4] * cfa[indx + w4]);
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float H_Stat = max(epssq, - 18.0f * cfa[indx] * cfa[indx - 1] - 18.0f * cfa[indx] * cfa[indx + 1] - 36.0f * cfa[indx] * cfa[indx - 2] - 36.0f * cfa[indx] * cfa[indx + 2] + 18.0f * cfa[indx] * cfa[indx - 3] + 18.0f * cfa[indx] * cfa[indx + 3] - 2.0f * cfa[indx] * cfa[indx - 4] - 2.0f * cfa[indx] * cfa[indx + 4] + 38.0f * cfa[indx] * cfa[indx] - 70.0f * cfa[indx - 1] * cfa[indx + 1] - 12.0f * cfa[indx - 1] * cfa[indx - 2] + 24.0f * cfa[indx - 1] * cfa[indx + 2] - 38.0f * cfa[indx - 1] * cfa[indx - 3] + 16.0f * cfa[indx - 1] * cfa[indx + 3] + 12.0f * cfa[indx - 1] * cfa[indx - 4] - 6.0f * cfa[indx - 1] * cfa[indx + 4] + 46.0f * cfa[indx - 1] * cfa[indx - 1] + 24.0f * cfa[indx + 1] * cfa[indx - 2] - 12.0f * cfa[indx + 1] * cfa[indx + 2] + 16.0f * cfa[indx + 1] * cfa[indx - 3] - 38.0f * cfa[indx + 1] * cfa[indx + 3] - 6.0f * cfa[indx + 1] * cfa[indx - 4] + 12.0f * cfa[indx + 1] * cfa[indx + 4] + 46.0f * cfa[indx + 1] * cfa[indx + 1] + 14.0f * cfa[indx - 2] * cfa[indx + 2] - 12.0f * cfa[indx - 2] * cfa[indx + 3] - 2.0f * cfa[indx - 2] * cfa[indx - 4] + 2.0f * cfa[indx - 2] * cfa[indx + 4] + 11.0f * cfa[indx - 2] * cfa[indx - 2] - 12.0f * cfa[indx + 2] * cfa[indx - 3] + 2.0f * cfa[indx + 2] * cfa[indx - 4] - 2.0f * cfa[indx + 2] * cfa[indx + 4] + 11.0f * cfa[indx + 2] * cfa[indx + 2] + 2.0f * cfa[indx - 3] * cfa[indx + 3] - 6.0f * cfa[indx - 3] * cfa[indx - 4] + 10.0f * cfa[indx - 3] * cfa[indx - 3] - 6.0f * cfa[indx + 3] * cfa[indx + 4] + 10.0f * cfa[indx + 3] * cfa[indx + 3] + 1.0f * cfa[indx - 4] * cfa[indx - 4] + 1.0f * cfa[indx + 4] * cfa[indx + 4]);
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VH_Dir[indx] = V_Stat / (V_Stat + H_Stat);
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}
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}
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/**
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* STEP 2: Calculate the low pass filter
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*/
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// Step 2.1: Low pass filter incorporating green, red and blue local samples from the raw data
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for ( int row = 2; row < tileSize - 2; row++ ) {
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for ( int col = 2 + (FC( row, 0 ) & 1), indx = row * tileSize + col; col < tileSize - 2; col += 2, indx += 2 ) {
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lpf[indx>>1] = 0.25f * cfa[indx] + 0.125f * ( cfa[indx - w1] + cfa[indx + w1] + cfa[indx - 1] + cfa[indx + 1] ) + 0.0625f * ( cfa[indx - w1 - 1] + cfa[indx - w1 + 1] + cfa[indx + w1 - 1] + cfa[indx + w1 + 1] );
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}
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}
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/**
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* STEP 3: Populate the green channel
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*/
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// Step 3.1: Populate the green channel at blue and red CFA positions
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for ( int row = 4; row < tileSize - 4; row++ ) {
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for ( int col = 4 + (FC( row, 0 ) & 1), indx = row * tileSize + col; col < tileSize - 4; col += 2, indx += 2 ) {
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// Refined vertical and horizontal local discrimination
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float VH_Central_Value = VH_Dir[indx];
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float VH_Neighbourhood_Value = 0.25f * ((VH_Dir[indx - w1 - 1] + VH_Dir[indx - w1 + 1]) + (VH_Dir[indx + w1 - 1] + VH_Dir[indx + w1 + 1]));
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float VH_Disc = std::fabs(0.5f - VH_Central_Value) < std::fabs(0.5f - VH_Neighbourhood_Value) ? VH_Neighbourhood_Value : VH_Central_Value;
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// Cardinal gradients
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float N_Grad = eps + std::fabs( cfa[indx - w1] - cfa[indx + w1] ) + std::fabs( cfa[indx] - cfa[indx - w2] ) + std::fabs( cfa[indx - w1] - cfa[indx - w3] ) + std::fabs( cfa[indx - w2] - cfa[indx - w4] );
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float S_Grad = eps + std::fabs( cfa[indx - w1] - cfa[indx + w1] ) + std::fabs( cfa[indx] - cfa[indx + w2] ) + std::fabs( cfa[indx + w1] - cfa[indx + w3] ) + std::fabs( cfa[indx + w2] - cfa[indx + w4] );
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float W_Grad = eps + std::fabs( cfa[indx - 1] - cfa[indx + 1] ) + std::fabs( cfa[indx] - cfa[indx - 2] ) + std::fabs( cfa[indx - 1] - cfa[indx - 3] ) + std::fabs( cfa[indx - 2] - cfa[indx - 4] );
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float E_Grad = eps + std::fabs( cfa[indx - 1] - cfa[indx + 1] ) + std::fabs( cfa[indx] - cfa[indx + 2] ) + std::fabs( cfa[indx + 1] - cfa[indx + 3] ) + std::fabs( cfa[indx + 2] - cfa[indx + 4] );
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// Cardinal pixel estimations
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float N_Est = cfa[indx - w1] * ( 1.f + ( lpf[indx>>1] - lpf[(indx - w2)>>1] ) / ( eps + lpf[indx>>1] + lpf[(indx - w2)>>1] ) );
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float S_Est = cfa[indx + w1] * ( 1.f + ( lpf[indx>>1] - lpf[(indx + w2)>>1] ) / ( eps + lpf[indx>>1] + lpf[(indx + w2)>>1] ) );
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float W_Est = cfa[indx - 1] * ( 1.f + ( lpf[indx>>1] - lpf[(indx - 2)>>1] ) / ( eps + lpf[indx>>1] + lpf[(indx - 2)>>1] ) );
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float E_Est = cfa[indx + 1] * ( 1.f + ( lpf[indx>>1] - lpf[(indx + 2)>>1] ) / ( eps + lpf[indx>>1] + lpf[(indx + 2)>>1] ) );
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// Vertical and horizontal estimations
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float V_Est = ( S_Grad * N_Est + N_Grad * S_Est ) / (N_Grad + S_Grad );
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float H_Est = ( W_Grad * E_Est + E_Grad * W_Est ) / (E_Grad + W_Grad );
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// G@B and G@R interpolation
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rgb[1][indx] = VH_Disc * H_Est + ( 1.f - VH_Disc ) * V_Est;
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}
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}
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/**
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* STEP 4: Populate the red and blue channels
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*/
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// Step 4.1: Calculate P/Q diagonal local discrimination
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for ( int row = 4; row < tileSize - 4; row++ ) {
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for ( int col = 4 + (FC( row, 0 ) & 1), indx = row * tileSize + col; col < tileSize - 4; col += 2, indx += 2 ) {
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float P_Stat = max( - 18.f * cfa[indx] * cfa[indx - w1 - 1] - 18.f * cfa[indx] * cfa[indx + w1 + 1] - 36.f * cfa[indx] * cfa[indx - w2 - 2] - 36.f * cfa[indx] * cfa[indx + w2 + 2] + 18.f * cfa[indx] * cfa[indx - w3 - 3] + 18.f * cfa[indx] * cfa[indx + w3 + 3] - 2.f * cfa[indx] * cfa[indx - w4 - 4] - 2.f * cfa[indx] * cfa[indx + w4 + 4] + 38.f * cfa[indx] * cfa[indx] - 70.f * cfa[indx - w1 - 1] * cfa[indx + w1 + 1] - 12.f * cfa[indx - w1 - 1] * cfa[indx - w2 - 2] + 24.f * cfa[indx - w1 - 1] * cfa[indx + w2 + 2] - 38.f * cfa[indx - w1 - 1] * cfa[indx - w3 - 3] + 16.f * cfa[indx - w1 - 1] * cfa[indx + w3 + 3] + 12.f * cfa[indx - w1 - 1] * cfa[indx - w4 - 4] - 6.f * cfa[indx - w1 - 1] * cfa[indx + w4 + 4] + 46.f * cfa[indx - w1 - 1] * cfa[indx - w1 - 1] + 24.f * cfa[indx + w1 + 1] * cfa[indx - w2 - 2] - 12.f * cfa[indx + w1 + 1] * cfa[indx + w2 + 2] + 16.f * cfa[indx + w1 + 1] * cfa[indx - w3 - 3] - 38.f * cfa[indx + w1 + 1] * cfa[indx + w3 + 3] - 6.f * cfa[indx + w1 + 1] * cfa[indx - w4 - 4] + 12.f * cfa[indx + w1 + 1] * cfa[indx + w4 + 4] + 46.f * cfa[indx + w1 + 1] * cfa[indx + w1 + 1] + 14.f * cfa[indx - w2 - 2] * cfa[indx + w2 + 2] - 12.f * cfa[indx - w2 - 2] * cfa[indx + w3 + 3] - 2.f * cfa[indx - w2 - 2] * cfa[indx - w4 - 4] + 2.f * cfa[indx - w2 - 2] * cfa[indx + w4 + 4] + 11.f * cfa[indx - w2 - 2] * cfa[indx - w2 - 2] - 12.f * cfa[indx + w2 + 2] * cfa[indx - w3 - 3] + 2 * cfa[indx + w2 + 2] * cfa[indx - w4 - 4] - 2.f * cfa[indx + w2 + 2] * cfa[indx + w4 + 4] + 11.f * cfa[indx + w2 + 2] * cfa[indx + w2 + 2] + 2.f * cfa[indx - w3 - 3] * cfa[indx + w3 + 3] - 6.f * cfa[indx - w3 - 3] * cfa[indx - w4 - 4] + 10.f * cfa[indx - w3 - 3] * cfa[indx - w3 - 3] - 6.f * cfa[indx + w3 + 3] * cfa[indx + w4 + 4] + 10.f * cfa[indx + w3 + 3] * cfa[indx + w3 + 3] + 1.f * cfa[indx - w4 - 4] * cfa[indx - w4 - 4] + 1.f * cfa[indx + w4 + 4] * cfa[indx + w4 + 4], epssq );
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float Q_Stat = max( - 18.f * cfa[indx] * cfa[indx + w1 - 1] - 18.f * cfa[indx] * cfa[indx - w1 + 1] - 36.f * cfa[indx] * cfa[indx + w2 - 2] - 36.f * cfa[indx] * cfa[indx - w2 + 2] + 18.f * cfa[indx] * cfa[indx + w3 - 3] + 18.f * cfa[indx] * cfa[indx - w3 + 3] - 2.f * cfa[indx] * cfa[indx + w4 - 4] - 2.f * cfa[indx] * cfa[indx - w4 + 4] + 38.f * cfa[indx] * cfa[indx] - 70.f * cfa[indx + w1 - 1] * cfa[indx - w1 + 1] - 12.f * cfa[indx + w1 - 1] * cfa[indx + w2 - 2] + 24.f * cfa[indx + w1 - 1] * cfa[indx - w2 + 2] - 38.f * cfa[indx + w1 - 1] * cfa[indx + w3 - 3] + 16.f * cfa[indx + w1 - 1] * cfa[indx - w3 + 3] + 12.f * cfa[indx + w1 - 1] * cfa[indx + w4 - 4] - 6.f * cfa[indx + w1 - 1] * cfa[indx - w4 + 4] + 46.f * cfa[indx + w1 - 1] * cfa[indx + w1 - 1] + 24.f * cfa[indx - w1 + 1] * cfa[indx + w2 - 2] - 12.f * cfa[indx - w1 + 1] * cfa[indx - w2 + 2] + 16.f * cfa[indx - w1 + 1] * cfa[indx + w3 - 3] - 38.f * cfa[indx - w1 + 1] * cfa[indx - w3 + 3] - 6.f * cfa[indx - w1 + 1] * cfa[indx + w4 - 4] + 12.f * cfa[indx - w1 + 1] * cfa[indx - w4 + 4] + 46.f * cfa[indx - w1 + 1] * cfa[indx - w1 + 1] + 14.f * cfa[indx + w2 - 2] * cfa[indx - w2 + 2] - 12.f * cfa[indx + w2 - 2] * cfa[indx - w3 + 3] - 2.f * cfa[indx + w2 - 2] * cfa[indx + w4 - 4] + 2.f * cfa[indx + w2 - 2] * cfa[indx - w4 + 4] + 11.f * cfa[indx + w2 - 2] * cfa[indx + w2 - 2] - 12.f * cfa[indx - w2 + 2] * cfa[indx + w3 - 3] + 2 * cfa[indx - w2 + 2] * cfa[indx + w4 - 4] - 2.f * cfa[indx - w2 + 2] * cfa[indx - w4 + 4] + 11.f * cfa[indx - w2 + 2] * cfa[indx - w2 + 2] + 2.f * cfa[indx + w3 - 3] * cfa[indx - w3 + 3] - 6.f * cfa[indx + w3 - 3] * cfa[indx + w4 - 4] + 10.f * cfa[indx + w3 - 3] * cfa[indx + w3 - 3] - 6.f * cfa[indx - w3 + 3] * cfa[indx - w4 + 4] + 10.f * cfa[indx - w3 + 3] * cfa[indx - w3 + 3] + 1.f * cfa[indx + w4 - 4] * cfa[indx + w4 - 4] + 1.f * cfa[indx - w4 + 4] * cfa[indx - w4 + 4], epssq );
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PQ_Dir[indx] = P_Stat / ( P_Stat + Q_Stat );
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}
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}
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// Step 4.2: Populate the red and blue channels at blue and red CFA positions
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for ( int row = 4; row < tileSize - 4; row++ ) {
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for ( int col = 4 + (FC( row, 0 ) & 1), indx = row * tileSize + col, c = 2 - FC( row, col ); col < tileSize - 4; col += 2, indx += 2 ) {
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// Refined P/Q diagonal local discrimination
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float PQ_Central_Value = PQ_Dir[indx];
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float PQ_Neighbourhood_Value = 0.25f * ( PQ_Dir[indx - w1 - 1] + PQ_Dir[indx - w1 + 1] + PQ_Dir[indx + w1 - 1] + PQ_Dir[indx + w1 + 1] );
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float PQ_Disc = ( std::fabs( 0.5f - PQ_Central_Value ) < std::fabs( 0.5f - PQ_Neighbourhood_Value ) ) ? PQ_Neighbourhood_Value : PQ_Central_Value;
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// Diagonal gradients
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float NW_Grad = eps + std::fabs( rgb[c][indx - w1 - 1] - rgb[c][indx + w1 + 1]) + std::fabs( rgb[c][indx - w1 - 1] - rgb[c][indx - w3 - 3] ) + std::fabs( rgb[1][indx] - rgb[1][indx - w2 - 2] );
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float NE_Grad = eps + std::fabs( rgb[c][indx - w1 + 1] - rgb[c][indx + w1 - 1]) + std::fabs( rgb[c][indx - w1 + 1] - rgb[c][indx - w3 + 3] ) + std::fabs( rgb[1][indx] - rgb[1][indx - w2 + 2] );
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float SW_Grad = eps + std::fabs( rgb[c][indx - w1 + 1] - rgb[c][indx + w1 - 1]) + std::fabs( rgb[c][indx + w1 - 1] - rgb[c][indx + w3 - 3] ) + std::fabs( rgb[1][indx] - rgb[1][indx + w2 - 2] );
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float SE_Grad = eps + std::fabs( rgb[c][indx - w1 - 1] - rgb[c][indx + w1 + 1]) + std::fabs( rgb[c][indx + w1 + 1] - rgb[c][indx + w3 + 3] ) + std::fabs( rgb[1][indx] - rgb[1][indx + w2 + 2] );
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// Diagonal colour differences
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float NW_Est = rgb[c][indx - w1 - 1] - rgb[1][indx - w1 - 1];
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float NE_Est = rgb[c][indx - w1 + 1] - rgb[1][indx - w1 + 1];
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float SW_Est = rgb[c][indx + w1 - 1] - rgb[1][indx + w1 - 1];
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float SE_Est = rgb[c][indx + w1 + 1] - rgb[1][indx + w1 + 1];
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// P/Q estimations
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float P_Est = ( NW_Grad * SE_Est + SE_Grad * NW_Est ) / (NW_Grad + SE_Grad );
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float Q_Est = ( NE_Grad * SW_Est + SW_Grad * NE_Est ) / (NE_Grad + SW_Grad );
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// R@B and B@R interpolation
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rgb[c][indx] = rgb[1][indx] + ( 1.f - PQ_Disc ) * P_Est + PQ_Disc * Q_Est;
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}
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}
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// Step 4.3: Populate the red and blue channels at green CFA positions
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for ( int row = 4; row < tileSize - 4; row++ ) {
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for ( int col = 4 + (FC( row, 1 ) & 1), indx = row * tileSize + col; col < tileSize - 4; col += 2, indx += 2 ) {
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// Refined vertical and horizontal local discrimination
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float VH_Central_Value = VH_Dir[indx];
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float VH_Neighbourhood_Value = 0.25f * ( (VH_Dir[indx - w1 - 1] + VH_Dir[indx - w1 + 1]) + (VH_Dir[indx + w1 - 1] + VH_Dir[indx + w1 + 1]) );
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float VH_Disc = ( std::fabs( 0.5f - VH_Central_Value ) < std::fabs( 0.5f - VH_Neighbourhood_Value ) ) ? VH_Neighbourhood_Value : VH_Central_Value;
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float N1 = eps + std::fabs( rgb[1][indx] - rgb[1][indx - w2] );
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float S1 = eps + std::fabs( rgb[1][indx] - rgb[1][indx + w2] );
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float W1 = eps + std::fabs( rgb[1][indx] - rgb[1][indx - 2] );
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float E1 = eps + std::fabs( rgb[1][indx] - rgb[1][indx + 2] );
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for ( int c = 0; c <= 2; c += 2 ) {
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// Cardinal gradients
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float N_Grad = N1 + std::fabs( rgb[c][indx - w1] - rgb[c][indx + w1] ) + std::fabs( rgb[c][indx - w1] - rgb[c][indx - w3] );
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float S_Grad = S1 + std::fabs( rgb[c][indx - w1] - rgb[c][indx + w1] ) + std::fabs( rgb[c][indx + w1] - rgb[c][indx + w3] );
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float W_Grad = W1 + std::fabs( rgb[c][indx - 1] - rgb[c][indx + 1] ) + std::fabs( rgb[c][indx - 1] - rgb[c][indx - 3] );
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float E_Grad = E1 + std::fabs( rgb[c][indx - 1] - rgb[c][indx + 1] ) + std::fabs( rgb[c][indx + 1] - rgb[c][indx + 3] );
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// Cardinal colour differences
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float N_Est = rgb[c][indx - w1] - rgb[1][indx - w1];
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float S_Est = rgb[c][indx + w1] - rgb[1][indx + w1];
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float W_Est = rgb[c][indx - 1] - rgb[1][indx - 1];
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float E_Est = rgb[c][indx + 1] - rgb[1][indx + 1];
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// Vertical and horizontal estimations
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float V_Est = ( N_Grad * S_Est + S_Grad * N_Est ) / (N_Grad + S_Grad );
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float H_Est = ( E_Grad * W_Est + W_Grad * E_Est ) / (E_Grad + W_Grad );
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// R@G and B@G interpolation
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rgb[c][indx] = rgb[1][indx] + ( 1.f - VH_Disc ) * V_Est + VH_Disc * H_Est;
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}
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}
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}
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for (int row = rowStart + tileBorder; row < rowEnd - tileBorder; ++row) {
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for (int col = colStart + tileBorder; col < colEnd - tileBorder; ++col) {
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int idx = (row - rowStart) * tileSize + col - colStart ;
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red[row][col] = CLIP(rgb[0][idx] * 65535.f);
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green[row][col] = CLIP(rgb[1][idx] * 65535.f);
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blue[row][col] = CLIP(rgb[2][idx] * 65535.f);
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}
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}
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}
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}
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free(cfa);
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free(rgb);
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free(VH_Dir);
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free(PQ_Dir);
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}
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border_interpolate2(width, height, 8);
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if (plistener) {
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plistener->setProgress(1);
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}
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// -------------------------------------------------------------------------
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}
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} /* namespace */
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