309 lines
20 KiB
C++
309 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 "procparams.h"
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#include "../rtgui/multilangmgr.h"
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#include "opthelper.h"
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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(size_t chunkSize, bool measure)
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{
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std::unique_ptr<StopWatch> stop;
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if (measure) {
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std::cout << "Demosaicing " << W << "x" << H << " image using rcd with " << chunkSize << " tiles per thread" << std::endl;
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stop.reset(new StopWatch("rcd demosaic"));
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}
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volatile double progress = 0.0;
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if (plistener) {
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plistener->setProgressStr(Glib::ustring::compose(M("TP_RAW_DMETHOD_PROGRESSBAR"), RAWParams::BayerSensor::getMethodString(RAWParams::BayerSensor::Method::RCD)));
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plistener->setProgress(0);
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}
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constexpr int rcdBorder = 9;
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constexpr int tileSize = 214;
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constexpr int tileSizeN = tileSize - 2 * rcdBorder;
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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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int progresscounter = 0;
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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, chunkSize) 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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const int rowStart = tr * tileSizeN;
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const int rowEnd = std::min(rowStart + tileSize, H);
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if(rowStart + rcdBorder == rowEnd - rcdBorder) {
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continue;
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}
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const int colStart = tc * tileSizeN;
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const int colEnd = std::min(colStart + tileSize, W);
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if(colStart + rcdBorder == colEnd - rcdBorder) {
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continue;
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}
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const int tileRows = std::min(rowEnd - rowStart, tileSize);
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const int tilecols = std::min(colEnd - colStart, tileSize);
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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 < tileRows - 4; row++) {
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for (int col = 4, indx = row * tileSize + col; col < tilecols - 4; col++, indx++) {
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const float cfai = cfa[indx];
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//Calculate h/v local discrimination
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float V_Stat = max(epssq, -18.f * cfai * (cfa[indx - w1] + cfa[indx + w1] + 2.f * (cfa[indx - w2] + cfa[indx + w2]) - cfa[indx - w3] - cfa[indx + w3]) - 2.f * cfai * (cfa[indx - w4] + cfa[indx + w4] - 19.f * cfai) - cfa[indx - w1] * (70.f * cfa[indx + w1] + 12.f * cfa[indx - w2] - 24.f * cfa[indx + w2] + 38.f * cfa[indx - w3] - 16.f * cfa[indx + w3] - 12.f * cfa[indx - w4] + 6.f * cfa[indx + w4] - 46.f * cfa[indx - w1]) + cfa[indx + w1] * (24.f * cfa[indx - w2] - 12.f * cfa[indx + w2] + 16.f * cfa[indx - w3] - 38.f * cfa[indx + w3] - 6.f * cfa[indx - w4] + 12.f * cfa[indx + w4] + 46.f * cfa[indx + w1]) + cfa[indx - w2] * (14.f * cfa[indx + w2] - 12.f * cfa[indx + w3] - 2.f * cfa[indx - w4] + 2.f * cfa[indx + w4] + 11.f * cfa[indx - w2]) + cfa[indx + w2] * (-12.f * cfa[indx - w3] + 2.f * (cfa[indx - w4] - cfa[indx + w4]) + 11.f * cfa[indx + w2]) + cfa[indx - w3] * (2.f * cfa[indx + w3] - 6.f * cfa[indx - w4] + 10.f * cfa[indx - w3]) + cfa[indx + w3] * (-6.f * cfa[indx + w4] + 10.f * cfa[indx + w3]) + cfa[indx - w4] * cfa[indx - w4] + cfa[indx + w4] * cfa[indx + w4]);
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float H_Stat = max(epssq, -18.f * cfai * (cfa[indx - 1] + cfa[indx + 1] + 2.f * (cfa[indx - 2] + cfa[indx + 2]) - cfa[indx - 3] - cfa[indx + 3]) - 2.f * cfai * (cfa[indx - 4] + cfa[indx + 4] - 19.f * cfai) - cfa[indx - 1] * (70.f * cfa[indx + 1] + 12.f * cfa[indx - 2] - 24.f * cfa[indx + 2] + 38.f * cfa[indx - 3] - 16.f * cfa[indx + 3] - 12.f * cfa[indx - 4] + 6.f * cfa[indx + 4] - 46.f * cfa[indx - 1]) + cfa[indx + 1] * (24.f * cfa[indx - 2] - 12.f * cfa[indx + 2] + 16.f * cfa[indx - 3] - 38.f * cfa[indx + 3] - 6.f * cfa[indx - 4] + 12.f * cfa[indx + 4] + 46.f * cfa[indx + 1]) + cfa[indx - 2] * (14.f * cfa[indx + 2] - 12.f * cfa[indx + 3] - 2.f * cfa[indx - 4] + 2.f * cfa[indx + 4] + 11.f * cfa[indx - 2]) + cfa[indx + 2] * (-12.f * cfa[indx - 3] + 2.f * (cfa[indx - 4] - cfa[indx + 4]) + 11.f * cfa[indx + 2]) + cfa[indx - 3] * (2.f * cfa[indx + 3] - 6.f * cfa[indx - 4] + 10.f * cfa[indx - 3]) + cfa[indx + 3] * (-6.f * cfa[indx + 4] + 10.f * cfa[indx + 3]) + cfa[indx - 4] * cfa[indx - 4] + 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 < tileRows - 2; row++) {
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for (int col = 2 + (FC(row, 0) & 1), indx = row * tileSize + col; col < tilecols - 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 < tileRows - 4; row++) {
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for (int col = 4 + (FC(row, 0) & 1), indx = row * tileSize + col; col < tilecols - 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 = rcdBorder - 4; row < tileRows - rcdBorder + 4; row++) {
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for (int col = rcdBorder - 4 + (FC(row, rcdBorder) & 1), indx = row * tileSize + col; col < tilecols - rcdBorder + 4; col += 2, indx += 2) {
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const float cfai = cfa[indx];
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float P_Stat = max(epssq, - 18.f * cfai * (cfa[indx - w1 - 1] + cfa[indx + w1 + 1] + 2.f * (cfa[indx - w2 - 2] + cfa[indx + w2 + 2]) - cfa[indx - w3 - 3] - cfa[indx + w3 + 3]) - 2.f * cfai * (cfa[indx - w4 - 4] + cfa[indx + w4 + 4] - 19.f * cfai) - cfa[indx - w1 - 1] * (70.f * cfa[indx + w1 + 1] - 12.f * cfa[indx - w2 - 2] + 24.f * cfa[indx + w2 + 2] - 38.f * cfa[indx - w3 - 3] + 16.f * cfa[indx + w3 + 3] + 12.f * cfa[indx - w4 - 4] - 6.f * cfa[indx + w4 + 4] + 46.f * cfa[indx - w1 - 1]) + cfa[indx + w1 + 1] * (24.f * cfa[indx - w2 - 2] - 12.f * cfa[indx + w2 + 2] + 16.f * cfa[indx - w3 - 3] - 38.f * cfa[indx + w3 + 3] - 6.f * cfa[indx - w4 - 4] + 12.f * cfa[indx + w4 + 4] + 46.f * cfa[indx + w1 + 1]) + cfa[indx - w2 - 2] * (14.f * cfa[indx + w2 + 2] - 12.f * cfa[indx + w3 + 3] - 2.f * (cfa[indx - w4 - 4] - cfa[indx + w4 + 4]) + 11.f * cfa[indx - w2 - 2]) - cfa[indx + w2 + 2] * (12.f * cfa[indx - w3 - 3] + 2.f * (cfa[indx - w4 - 4] - cfa[indx + w4 + 4]) + 11.f * cfa[indx + w2 + 2]) + cfa[indx - w3 - 3] * (2.f * cfa[indx + w3 + 3] - 6.f * cfa[indx - w4 - 4] + 10.f * cfa[indx - w3 - 3]) - cfa[indx + w3 + 3] * (6.f * cfa[indx + w4 + 4] + 10.f * cfa[indx + w3 + 3]) + cfa[indx - w4 - 4] * cfa[indx - w4 - 4] + cfa[indx + w4 + 4] * cfa[indx + w4 + 4]);
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float Q_Stat = max(epssq, - 18.f * cfai * (cfa[indx + w1 - 1] + cfa[indx - w1 + 1] + 2.f * (cfa[indx + w2 - 2] + cfa[indx - w2 + 2]) - cfa[indx + w3 - 3] - cfa[indx - w3 + 3]) - 2.f * cfai * (cfa[indx + w4 - 4] + cfa[indx - w4 + 4] - 19.f * cfai) - cfa[indx + w1 - 1] * (70.f * cfa[indx - w1 + 1] - 12.f * cfa[indx + w2 - 2] + 24.f * cfa[indx - w2 + 2] - 38.f * cfa[indx + w3 - 3] + 16.f * cfa[indx - w3 + 3] + 12.f * cfa[indx + w4 - 4] - 6.f * cfa[indx - w4 + 4] + 46.f * cfa[indx + w1 - 1]) + cfa[indx - w1 + 1] * (24.f * cfa[indx + w2 - 2] - 12.f * cfa[indx - w2 + 2] + 16.f * cfa[indx + w3 - 3] - 38.f * cfa[indx - w3 + 3] - 6.f * cfa[indx + w4 - 4] + 12.f * cfa[indx - w4 + 4] + 46.f * cfa[indx - w1 + 1]) + cfa[indx + w2 - 2] * (14.f * cfa[indx - w2 + 2] - 12.f * cfa[indx - w3 + 3] - 2.f * (cfa[indx + w4 - 4] - cfa[indx - w4 + 4]) + 11.f * cfa[indx + w2 - 2]) - cfa[indx - w2 + 2] * (12.f * cfa[indx + w3 - 3] + 2.f * (cfa[indx + w4 - 4] - cfa[indx - w4 + 4]) + 11.f * cfa[indx - w2 + 2]) + cfa[indx + w3 - 3] * (2.f * cfa[indx - w3 + 3] - 6.f * cfa[indx + w4 - 4] + 10.f * cfa[indx + w3 - 3]) - cfa[indx - w3 + 3] * (6.f * cfa[indx - w4 + 4] + 10.f * cfa[indx - w3 + 3]) + cfa[indx + w4 - 4] * cfa[indx + w4 - 4] + cfa[indx - w4 + 4] * cfa[indx - w4 + 4]);
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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 = rcdBorder - 3; row < tileRows - rcdBorder + 3; row++) {
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for (int col = rcdBorder - 3 + (FC(row, rcdBorder - 1) & 1), indx = row * tileSize + col, c = 2 - FC(row, col); col < tilecols - rcdBorder + 3; 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 = rcdBorder; row < tileRows - rcdBorder; row++) {
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for (int col = rcdBorder + (FC(row, rcdBorder - 1) & 1), indx = row * tileSize + col; col < tilecols - rcdBorder; 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 rgb1 = rgb[1][indx];
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float N1 = eps + std::fabs(rgb1 - rgb[1][indx - w2]);
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float S1 = eps + std::fabs(rgb1 - rgb[1][indx + w2]);
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float W1 = eps + std::fabs(rgb1 - rgb[1][indx - 2]);
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float E1 = eps + std::fabs(rgb1 - rgb[1][indx + 2]);
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float rgb1mw1 = rgb[1][indx - w1];
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float rgb1pw1 = rgb[1][indx + w1];
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float rgb1m1 = rgb[1][indx - 1];
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float rgb1p1 = rgb[1][indx + 1];
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for (int c = 0; c <= 2; c += 2) {
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// Cardinal gradients
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float SNabs = std::fabs(rgb[c][indx - w1] - rgb[c][indx + w1]);
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float EWabs = std::fabs(rgb[c][indx - 1] - rgb[c][indx + 1]);
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float N_Grad = N1 + SNabs + std::fabs(rgb[c][indx - w1] - rgb[c][indx - w3]);
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float S_Grad = S1 + SNabs + std::fabs(rgb[c][indx + w1] - rgb[c][indx + w3]);
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float W_Grad = W1 + EWabs + std::fabs(rgb[c][indx - 1] - rgb[c][indx - 3]);
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float E_Grad = E1 + EWabs + 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] - rgb1mw1;
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float S_Est = rgb[c][indx + w1] - rgb1pw1;
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float W_Est = rgb[c][indx - 1] - rgb1m1;
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float E_Est = rgb[c][indx + 1] - rgb1p1;
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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] = rgb1 + (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 + rcdBorder; row < rowEnd - rcdBorder; ++row) {
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for (int col = colStart + rcdBorder; col < colEnd - rcdBorder; ++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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if(plistener) {
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progresscounter++;
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if(progresscounter % 32 == 0) {
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#ifdef _OPENMP
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#pragma omp critical (rcdprogress)
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#endif
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{
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progress += (double)32 * ((tileSizeN) * (tileSizeN)) / (H * W);
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progress = progress > 1.0 ? 1.0 : progress;
|
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plistener->setProgress(progress);
|
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}
|
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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(W, H, rcdBorder, rawData, red, green, blue);
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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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