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Merge pull request #6064 from Beep6581/rcd_new

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Rcd improvements
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Ingo Weyrich 2021-01-19 10:59:18 +01:00 committed by GitHub
commit 049a6143f0
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1 changed files with 82 additions and 98 deletions
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rtengine/rcd_demosaic.cc
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@ -21,7 +21,6 @@
#include "rawimagesource.h"
#include "rt_math.h"
#include "../rtgui/multilangmgr.h"
#include "opthelper.h"
#include "StopWatch.h"
using namespace std;
@ -36,7 +35,7 @@ unsigned fc(const unsigned int cfa[2][2], int r, int c) {
namespace rtengine
{
/**
/*
* RATIO CORRECTED DEMOSAICING
* Luis Sanz Rodriguez (luis.sanz.rodriguez(at)gmail(dot)com)
*
@ -45,7 +44,12 @@ namespace rtengine
* Original code from https://github.com/LuisSR/RCD-Demosaicing
* Licensed under the GNU GPL version 3
*/
// Tiled version by Ingo Weyrich (heckflosse67@gmx.de)
// Luis Sanz Rodriguez significantly optimised the v 2.3 code and simplified the directional
// coefficients in an exact, shorter and more performant formula.
// In cooperation with Hanno Schwalm (hanno@schwalm-bremen.de) and Luis Sanz Rodriguez this has been tuned for performance.
void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
{
// Test for RGB cfa
@ -61,7 +65,6 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
}
std::unique_ptr<StopWatch> stop;
if (measure) {
std::cout << "Demosaicing " << W << "x" << H << " image using rcd with " << chunkSize << " tiles per thread" << std::endl;
stop.reset(new StopWatch("rcd demosaic"));
@ -75,9 +78,10 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
}
const unsigned int cfarray[2][2] = {{FC(0,0), FC(0,1)}, {FC(1,0), FC(1,1)}};
constexpr int rcdBorder = 9;
constexpr int tileSize = 214;
constexpr int tileSizeN = tileSize - 2 * rcdBorder;
constexpr int tileBorder = 9; // avoid tile-overlap errors
constexpr int rcdBorder = 6; // for the outermost tiles we can have a smaller outer border
constexpr int tileSize = 194;
constexpr int tileSizeN = tileSize - 2 * tileBorder;
const int numTh = H / (tileSizeN) + ((H % (tileSizeN)) ? 1 : 0);
const int numTw = W / (tileSizeN) + ((W % (tileSizeN)) ? 1 : 0);
constexpr int w1 = tileSize, w2 = 2 * tileSize, w3 = 3 * tileSize, w4 = 4 * tileSize;
@ -96,6 +100,8 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
float *const VH_Dir = (float*) calloc(tileSize * tileSize, sizeof *VH_Dir);
float *const PQ_Dir = (float*) calloc(tileSize * tileSize / 2, sizeof *PQ_Dir);
float *const lpf = PQ_Dir; // reuse buffer, they don't overlap in usage
float *const P_CDiff_Hpf = (float*) calloc(tileSize * tileSize / 2, sizeof *P_CDiff_Hpf);
float *const Q_CDiff_Hpf = (float*) calloc(tileSize * tileSize / 2, sizeof *Q_CDiff_Hpf);
#ifdef _OPENMP
#pragma omp for schedule(dynamic, chunkSize) collapse(2) nowait
@ -104,12 +110,12 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
for (int tc = 0; tc < numTw; ++tc) {
const int rowStart = tr * tileSizeN;
const int rowEnd = std::min(rowStart + tileSize, H);
if (rowStart + rcdBorder == rowEnd - rcdBorder) {
if (rowStart + tileBorder == rowEnd - tileBorder) {
continue;
}
const int colStart = tc * tileSizeN;
const int colEnd = std::min(colStart + tileSize, W);
if (colStart + rcdBorder == colEnd - rcdBorder) {
if (colStart + tileBorder == colEnd - tileBorder) {
continue;
}
@ -117,93 +123,59 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
const int tilecols = std::min(colEnd - colStart, tileSize);
for (int row = rowStart; row < rowEnd; row++) {
int indx = (row - rowStart) * tileSize;
const int c0 = fc(cfarray, row, colStart);
const int c1 = fc(cfarray, row, colStart + 1);
int col = colStart;
for (; col < colEnd - 1; col+=2, indx+=2) {
cfa[indx] = rgb[c0][indx] = LIM01(rawData[row][col] / scale);
cfa[indx + 1] = rgb[c1][indx + 1] = LIM01(rawData[row][col + 1] / scale);
}
if (col < colEnd) {
cfa[indx] = rgb[c0][indx] = LIM01(rawData[row][col] / scale);
for (int col = colStart, indx = (row - rowStart) * tileSize; col < colEnd; ++col, ++indx) {
cfa[indx] = rgb[c0][indx] = rgb[c1][indx] = LIM01(rawData[row][col] / scale);
}
}
/**
* STEP 1: Find cardinal and diagonal interpolation directions
*/
// Step 1: Find cardinal and diagonal interpolation directions
float bufferV[3][tileSize - 8];
for (int row = 4; row < tileRows - 4; row++) {
for (int col = 4, indx = row * tileSize + col; col < tilecols - 4; col++, indx++) {
const float cfai = cfa[indx];
//Calculate h/v local discrimination
float V_Stat = std::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]);
float H_Stat = std::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]);
// Step 1.1: Calculate the square of the vertical and horizontal color difference high pass filter
for (int row = 3; row < std::min(tileRows - 3, 5); ++row) {
for (int col = 4, indx = row * tileSize + col; col < tilecols - 4; ++col, ++indx) {
bufferV[row - 3][col - 4] = SQR((cfa[indx - w3] - cfa[indx - w1] - cfa[indx + w1] + cfa[indx + w3]) - 3.f * (cfa[indx - w2] + cfa[indx + w2]) + 6.f * cfa[indx]);
}
}
// Step 1.2: Obtain the vertical and horizontal directional discrimination strength
float bufferH[tileSize - 6] ALIGNED16;
float* V0 = bufferV[0];
float* V1 = bufferV[1];
float* V2 = bufferV[2];
for (int row = 4; row < tileRows - 4; ++row) {
for (int col = 3, indx = row * tileSize + col; col < tilecols - 3; ++col, ++indx) {
bufferH[col - 3] = SQR((cfa[indx - 3] - cfa[indx - 1] - cfa[indx + 1] + cfa[indx + 3]) - 3.f * (cfa[indx - 2] + cfa[indx + 2]) + 6.f * cfa[indx]);
}
for (int col = 4, indx = (row + 1) * tileSize + col; col < tilecols - 4; ++col, ++indx) {
V2[col - 4] = SQR((cfa[indx - w3] - cfa[indx - w1] - cfa[indx + w1] + cfa[indx + w3]) - 3.f * (cfa[indx - w2] + cfa[indx + w2]) + 6.f * cfa[indx]);
}
for (int col = 4, indx = row * tileSize + col; col < tilecols - 4; ++col, ++indx) {
float V_Stat = std::max(epssq, V0[col - 4] + V1[col - 4] + V2[col - 4]);
float H_Stat = std::max(epssq, bufferH[col - 4] + bufferH[col - 3] + bufferH[col - 2]);
VH_Dir[indx] = V_Stat / (V_Stat + H_Stat);
}
// rotate pointers from row0, row1, row2 to row1, row2, row0
std::swap(V0, V2);
std::swap(V0, V1);
}
/**
* STEP 2: Calculate the low pass filter
*/
// Step 2.1: Low pass filter incorporating green, red and blue local samples from the raw data
for (int row = 2; row < tileRows - 2; row++) {
// Step 2: Low pass filter incorporating green, red and blue local samples from the raw data
for (int row = 2; row < tileRows - 2; ++row) {
for (int col = 2 + (fc(cfarray, row, 0) & 1), indx = row * tileSize + col, lpindx = indx / 2; col < tilecols - 2; col += 2, indx += 2, ++lpindx) {
lpf[lpindx] = 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]);
lpf[lpindx] = cfa[indx] +
0.5f * (cfa[indx - w1] + cfa[indx + w1] + cfa[indx - 1] + cfa[indx + 1]) +
0.25f * (cfa[indx - w1 - 1] + cfa[indx - w1 + 1] + cfa[indx + w1 - 1] + cfa[indx + w1 + 1]);
}
}
/**
* STEP 3: Populate the green channel
*/
// Step 3.1: Populate the green channel at blue and red CFA positions
for (int row = 4; row < tileRows - 4; row++) {
int col = 4 + (fc(cfarray, row, 0) & 1);
int indx = row * tileSize + col;
int lpindx = indx / 2;
#ifdef __SSE2__
const vfloat zd5v = F2V(0.5f);
const vfloat zd25v = F2V(0.25f);
const vfloat epsv = F2V(eps);
for (; col < tilecols - 7; col += 8, indx += 8, lpindx += 4) {
// Cardinal gradients
const vfloat cfai = LC2VFU(cfa[indx]);
const vfloat N_Grad = epsv + (vabsf(LC2VFU(cfa[indx - w1]) - LC2VFU(cfa[indx + w1])) + vabsf(cfai - LC2VFU(cfa[indx - w2]))) + (vabsf(LC2VFU(cfa[indx - w1]) - LC2VFU(cfa[indx - w3])) + vabsf(LC2VFU(cfa[indx - w2]) - LC2VFU(cfa[indx - w4])));
const vfloat S_Grad = epsv + (vabsf(LC2VFU(cfa[indx - w1]) - LC2VFU(cfa[indx + w1])) + vabsf(cfai - LC2VFU(cfa[indx + w2]))) + (vabsf(LC2VFU(cfa[indx + w1]) - LC2VFU(cfa[indx + w3])) + vabsf(LC2VFU(cfa[indx + w2]) - LC2VFU(cfa[indx + w4])));
const vfloat W_Grad = epsv + (vabsf(LC2VFU(cfa[indx - 1]) - LC2VFU(cfa[indx + 1])) + vabsf(cfai - LC2VFU(cfa[indx - 2]))) + (vabsf(LC2VFU(cfa[indx - 1]) - LC2VFU(cfa[indx - 3])) + vabsf(LC2VFU(cfa[indx - 2]) - LC2VFU(cfa[indx - 4])));
const vfloat E_Grad = epsv + (vabsf(LC2VFU(cfa[indx - 1]) - LC2VFU(cfa[indx + 1])) + vabsf(cfai - LC2VFU(cfa[indx + 2]))) + (vabsf(LC2VFU(cfa[indx + 1]) - LC2VFU(cfa[indx + 3])) + vabsf(LC2VFU(cfa[indx + 2]) - LC2VFU(cfa[indx + 4])));
// Cardinal pixel estimations
const vfloat lpfi = LVFU(lpf[lpindx]);
const vfloat N_Est = LC2VFU(cfa[indx - w1]) + (LC2VFU(cfa[indx - w1]) * (lpfi - LVFU(lpf[lpindx - w1])) / (epsv + lpfi + LVFU(lpf[lpindx - w1])));
const vfloat S_Est = LC2VFU(cfa[indx + w1]) + (LC2VFU(cfa[indx + w1]) * (lpfi - LVFU(lpf[lpindx + w1])) / (epsv + lpfi + LVFU(lpf[lpindx + w1])));
const vfloat W_Est = LC2VFU(cfa[indx - 1]) + (LC2VFU(cfa[indx - 1]) * (lpfi - LVFU(lpf[lpindx - 1])) / (epsv + lpfi + LVFU(lpf[lpindx - 1])));
const vfloat E_Est = LC2VFU(cfa[indx + 1]) + (LC2VFU(cfa[indx + 1]) * (lpfi - LVFU(lpf[lpindx + 1])) / (epsv + lpfi + LVFU(lpf[lpindx + 1])));
// Vertical and horizontal estimations
const vfloat V_Est = (S_Grad * N_Est + N_Grad * S_Est) / (N_Grad + S_Grad);
const vfloat H_Est = (W_Grad * E_Est + E_Grad * W_Est) / (E_Grad + W_Grad);
// G@B and G@R interpolation
// Refined vertical and horizontal local discrimination
const vfloat VH_Central_Value = LC2VFU(VH_Dir[indx]);
const vfloat VH_Neighbourhood_Value = zd25v * ((LC2VFU(VH_Dir[indx - w1 - 1]) + LC2VFU(VH_Dir[indx - w1 + 1])) + (LC2VFU(VH_Dir[indx + w1 - 1]) + LC2VFU(VH_Dir[indx + w1 + 1])));
#if defined(__clang__)
const vfloat VH_Disc = vself(vmaskf_lt(vabsf(zd5v - VH_Central_Value), vabsf(zd5v - VH_Neighbourhood_Value)), VH_Neighbourhood_Value, VH_Central_Value);
#else
const vfloat VH_Disc = vabsf(zd5v - VH_Central_Value) < vabsf(zd5v - VH_Neighbourhood_Value) ? VH_Neighbourhood_Value : VH_Central_Value;
#endif
const vfloat result = vintpf(VH_Disc, H_Est, V_Est);
STC2VFU(rgb[1][indx], result);
}
#endif
for (; col < tilecols - 4; col += 2, indx += 2, ++lpindx) {
// Step 3: Populate the green channel at blue and red CFA positions
for (int row = 4; row < tileRows - 4; ++row) {
for (int col = 4 + (fc(cfarray, row, 0) & 1), indx = row * tileSize + col, lpindx = indx / 2; col < tilecols - 4; col += 2, indx += 2, ++lpindx) {
// Cardinal gradients
const float cfai = cfa[indx];
const float N_Grad = eps + (std::fabs(cfa[indx - w1] - cfa[indx + w1]) + std::fabs(cfai - cfa[indx - w2])) + (std::fabs(cfa[indx - w1] - cfa[indx - w3]) + std::fabs(cfa[indx - w2] - cfa[indx - w4]));
@ -213,10 +185,10 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
// Cardinal pixel estimations
const float lpfi = lpf[lpindx];
const float N_Est = cfa[indx - w1] * (1.f + (lpfi - lpf[lpindx - w1]) / (eps + lpfi + lpf[lpindx - w1]));
const float S_Est = cfa[indx + w1] * (1.f + (lpfi - lpf[lpindx + w1]) / (eps + lpfi + lpf[lpindx + w1]));
const float W_Est = cfa[indx - 1] * (1.f + (lpfi - lpf[lpindx - 1]) / (eps + lpfi + lpf[lpindx - 1]));
const float E_Est = cfa[indx + 1] * (1.f + (lpfi - lpf[lpindx + 1]) / (eps + lpfi + lpf[lpindx + 1]));
const float N_Est = cfa[indx - w1] * (lpfi + lpfi) / (eps + lpfi + lpf[lpindx - w1]);
const float S_Est = cfa[indx + w1] * (lpfi + lpfi) / (eps + lpfi + lpf[lpindx + w1]);
const float W_Est = cfa[indx - 1] * (lpfi + lpfi) / (eps + lpfi + lpf[lpindx - 1]);
const float E_Est = cfa[indx + 1] * (lpfi + lpfi) / (eps + lpfi + lpf[lpindx + 1]);
// Vertical and horizontal estimations
const float V_Est = (S_Grad * N_Est + N_Grad * S_Est) / (N_Grad + S_Grad);
@ -236,21 +208,26 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
* STEP 4: Populate the red and blue channels
*/
// Step 4.1: Calculate P/Q diagonal local discrimination
for (int row = rcdBorder - 4; row < tileRows - rcdBorder + 4; row++) {
for (int col = rcdBorder - 4 + (fc(cfarray, row, rcdBorder) & 1), indx = row * tileSize + col, pqindx = indx / 2; col < tilecols - rcdBorder + 4; col += 2, indx += 2, ++pqindx) {
const float cfai = cfa[indx];
// Step 4.0: Calculate the square of the P/Q diagonals color difference high pass filter
for (int row = 3; row < tileRows - 3; ++row) {
for (int col = 3, indx = row * tileSize + col, indx2 = indx / 2; col < tilecols - 3; col+=2, indx+=2, indx2++ ) {
P_CDiff_Hpf[indx2] = SQR((cfa[indx - w3 - 3] - cfa[indx - w1 - 1] - cfa[indx + w1 + 1] + cfa[indx + w3 + 3]) - 3.f * (cfa[indx - w2 - 2] + cfa[indx + w2 + 2]) + 6.f * cfa[indx]);
Q_CDiff_Hpf[indx2] = SQR((cfa[indx - w3 + 3] - cfa[indx - w1 + 1] - cfa[indx + w1 - 1] + cfa[indx + w3 - 3]) - 3.f * (cfa[indx - w2 + 2] + cfa[indx + w2 - 2]) + 6.f * cfa[indx]);
}
}
float P_Stat = std::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]);
float Q_Stat = std::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]);
PQ_Dir[pqindx] = P_Stat / (P_Stat + Q_Stat);
// Step 4.1: Obtain the P/Q diagonals directional discrimination strength
for (int row = 4; row < tileRows - 4; ++row) {
for (int col = 4 + (fc(cfarray, row, 0) & 1), indx = row * tileSize + col, indx2 = indx / 2, indx3 = (indx - w1 - 1) / 2, indx4 = (indx + w1 - 1) / 2; col < tilecols - 4; col += 2, indx += 2, indx2++, indx3++, indx4++ ) {
float P_Stat = std::max(epssq, P_CDiff_Hpf[indx3] + P_CDiff_Hpf[indx2] + P_CDiff_Hpf[indx4 + 1]);
float Q_Stat = std::max(epssq, Q_CDiff_Hpf[indx3 + 1] + Q_CDiff_Hpf[indx2] + Q_CDiff_Hpf[indx4]);
PQ_Dir[indx2] = P_Stat / (P_Stat + Q_Stat);
}
}
// Step 4.2: Populate the red and blue channels at blue and red CFA positions
for (int row = rcdBorder - 3; row < tileRows - rcdBorder + 3; row++) {
for (int col = rcdBorder - 3 + (fc(cfarray, row, rcdBorder - 1) & 1), indx = row * tileSize + col, c = 2 - fc(cfarray, row, col), pqindx = indx / 2, pqindx2 = (indx - w1 - 1) / 2, pqindx3 = (indx + w1 - 1) / 2; col < tilecols - rcdBorder + 3; col += 2, indx += 2, ++pqindx, ++pqindx2, ++pqindx3) {
for (int row = 4; row < tileRows - 4; ++row) {
for (int col = 4 + (fc(cfarray, row, 0) & 1), indx = row * tileSize + col, c = 2 - fc(cfarray, row, col), pqindx = indx / 2, pqindx2 = (indx - w1 - 1) / 2, pqindx3 = (indx + w1 - 1) / 2; col < tilecols - 4; col += 2, indx += 2, ++pqindx, ++pqindx2, ++pqindx3) {
// Refined P/Q diagonal local discrimination
float PQ_Central_Value = PQ_Dir[pqindx];
@ -280,8 +257,8 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
}
// Step 4.3: Populate the red and blue channels at green CFA positions
for (int row = rcdBorder; row < tileRows - rcdBorder; row++) {
for (int col = rcdBorder + (fc(cfarray, row, rcdBorder - 1) & 1), indx = row * tileSize + col; col < tilecols - rcdBorder; col += 2, indx += 2) {
for (int row = 4; row < tileRows - 4; ++row) {
for (int col = 4 + (fc(cfarray, row, 1) & 1), indx = row * tileSize + col; col < tilecols - 4; col += 2, indx += 2) {
// Refined vertical and horizontal local discrimination
float VH_Central_Value = VH_Dir[indx];
@ -323,8 +300,13 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
}
}
for (int row = rowStart + rcdBorder; row < rowEnd - rcdBorder; ++row) {
for (int col = colStart + rcdBorder; col < colEnd - rcdBorder; ++col) {
// For the outermost tiles in all directions we can use a smaller border margin
const int firstVertical = rowStart + ((tr == 0) ? rcdBorder : tileBorder);
const int lastVertical = rowEnd - ((tr == numTh - 1) ? rcdBorder : tileBorder);
const int firstHorizontal = colStart + ((tc == 0) ? rcdBorder : tileBorder);
const int lastHorizontal = colEnd - ((tc == numTw - 1) ? rcdBorder : tileBorder);
for (int row = firstVertical; row < lastVertical; ++row) {
for (int col = firstHorizontal; col < lastHorizontal; ++col) {
int idx = (row - rowStart) * tileSize + col - colStart ;
red[row][col] = std::max(0.f, rgb[0][idx] * scale);
green[row][col] = std::max(0.f, rgb[1][idx] * scale);
@ -352,6 +334,8 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
free(rgb);
free(VH_Dir);
free(PQ_Dir);
free(P_CDiff_Hpf);
free(Q_CDiff_Hpf);
}
border_interpolate(W, H, rcdBorder, rawData, red, green, blue);