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Improvement for rcd demosaic, #6054

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This commit is contained in:
Ingo Weyrich 2021-01-10 21:33:00 +01:00
parent 1fc3f036c1
commit f94cf78696
1 changed files with 73 additions and 76 deletions
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149
rtengine/rcd_demosaic.cc
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@ -21,7 +21,6 @@
#include "rawimagesource.h" #include "rawimagesource.h"
#include "rt_math.h" #include "rt_math.h"
#include "../rtgui/multilangmgr.h" #include "../rtgui/multilangmgr.h"
#include "opthelper.h"
#include "StopWatch.h" #include "StopWatch.h"
using namespace std; using namespace std;
@ -75,9 +74,10 @@ 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)}}; const unsigned int cfarray[2][2] = {{FC(0,0), FC(0,1)}, {FC(1,0), FC(1,1)}};
constexpr int rcdBorder = 9; constexpr int rcdBorder = 6;
constexpr int tileSize = 214; constexpr int tileBorder = 9;
constexpr int tileSizeN = tileSize - 2 * rcdBorder; constexpr int tileSize = 140;
constexpr int tileSizeN = tileSize - 2 * tileBorder;
const int numTh = H / (tileSizeN) + ((H % (tileSizeN)) ? 1 : 0); const int numTh = H / (tileSizeN) + ((H % (tileSizeN)) ? 1 : 0);
const int numTw = W / (tileSizeN) + ((W % (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; constexpr int w1 = tileSize, w2 = 2 * tileSize, w3 = 3 * tileSize, w4 = 4 * tileSize;
@ -96,6 +96,8 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
float *const VH_Dir = (float*) calloc(tileSize * tileSize, sizeof *VH_Dir); 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 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 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 #ifdef _OPENMP
#pragma omp for schedule(dynamic, chunkSize) collapse(2) nowait #pragma omp for schedule(dynamic, chunkSize) collapse(2) nowait
@ -104,12 +106,12 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
for (int tc = 0; tc < numTw; ++tc) { for (int tc = 0; tc < numTw; ++tc) {
const int rowStart = tr * tileSizeN; const int rowStart = tr * tileSizeN;
const int rowEnd = std::min(rowStart + tileSize, H); const int rowEnd = std::min(rowStart + tileSize, H);
if (rowStart + rcdBorder == rowEnd - rcdBorder) { if (rowStart + tileBorder == rowEnd - tileBorder) {
continue; continue;
} }
const int colStart = tc * tileSizeN; const int colStart = tc * tileSizeN;
const int colEnd = std::min(colStart + tileSize, W); const int colEnd = std::min(colStart + tileSize, W);
if (colStart + rcdBorder == colEnd - rcdBorder) { if (colStart + tileBorder == colEnd - tileBorder) {
continue; continue;
} }
@ -134,15 +136,38 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
* STEP 1: Find cardinal and diagonal interpolation directions * STEP 1: Find cardinal and diagonal interpolation directions
*/ */
for (int row = 4; row < tileRows - 4; row++) { float bufferV[3][tileSize - 8];
for (int col = 4, indx = row * tileSize + col; col < tilecols - 4; col++, indx++) {
const float cfai = cfa[indx]; // Step 1.1: Calculate the square of the vertical and horizontal color difference high pass filter
//Calculate h/v local discrimination for (int row = 3; row < std::min(tileRows - 3, 5); ++row) {
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]); for (int col = 4, indx = row * tileSize + col; col < tilecols - 4; ++col, ++indx) {
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]); 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); 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);
} }
/** /**
@ -150,11 +175,11 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
*/ */
// Step 2.1: Low pass filter incorporating green, red and blue local samples from the raw data // 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++) { 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) { 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] + lpf[lpindx] = cfa[indx] +
0.125f * (cfa[indx - w1] + cfa[indx + w1] + cfa[indx - 1] + cfa[indx + 1]) + 0.5f * (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]); 0.25f * (cfa[indx - w1 - 1] + cfa[indx - w1 + 1] + cfa[indx + w1 - 1] + cfa[indx + w1 + 1]);
} }
} }
@ -162,48 +187,8 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
* STEP 3: Populate the green channel * STEP 3: Populate the green channel
*/ */
// Step 3.1: Populate the green channel at blue and red CFA positions // Step 3.1: Populate the green channel at blue and red CFA positions
for (int row = 4; row < tileRows - 4; row++) { for (int row = 4; row < tileRows - 4; ++row) {
int col = 4 + (fc(cfarray, row, 0) & 1); for (int col = 4 + (fc(cfarray, row, 0) & 1), indx = row * tileSize + col, lpindx = indx / 2; col < tilecols - 4; col += 2, indx += 2, ++lpindx) {
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) {
// Cardinal gradients // Cardinal gradients
const float cfai = cfa[indx]; 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])); 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 +198,10 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
// Cardinal pixel estimations // Cardinal pixel estimations
const float lpfi = lpf[lpindx]; 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 N_Est = cfa[indx - w1] * (lpfi + lpfi) / (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 S_Est = cfa[indx + w1] * (lpfi + lpfi) / (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 W_Est = cfa[indx - 1] * (lpfi + lpfi) / (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 E_Est = cfa[indx + 1] * (lpfi + lpfi) / (eps + lpfi + lpf[lpindx + 1]);
// Vertical and horizontal estimations // Vertical and horizontal estimations
const float V_Est = (S_Grad * N_Est + N_Grad * S_Est) / (N_Grad + S_Grad); const float V_Est = (S_Grad * N_Est + N_Grad * S_Est) / (N_Grad + S_Grad);
@ -236,21 +221,26 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
* STEP 4: Populate the red and blue channels * STEP 4: Populate the red and blue channels
*/ */
// Step 4.1: Calculate P/Q diagonal local discrimination // Step 4.0: Calculate the square of the P/Q diagonals color difference high pass filter
for (int row = rcdBorder - 4; row < tileRows - rcdBorder + 4; row++) { for (int row = 3; row < tileRows - 3; ++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) { for (int col = 3, indx = row * tileSize + col, indx2 = indx / 2; col < tilecols - 3; col+=2, indx+=2, indx2++ ) {
const float cfai = cfa[indx]; 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]); // Step 4.1: Obtain the P/Q diagonals directional discrimination strength
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]); for (int row = 4; row < tileRows - 4; ++row) {
for (int col = 4 + FC(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++ ) {
PQ_Dir[pqindx] = P_Stat / (P_Stat + Q_Stat); 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 // 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 row = 4; row < tileRows - 4; ++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 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 // Refined P/Q diagonal local discrimination
float PQ_Central_Value = PQ_Dir[pqindx]; float PQ_Central_Value = PQ_Dir[pqindx];
@ -280,8 +270,8 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
} }
// Step 4.3: Populate the red and blue channels at green CFA positions // Step 4.3: Populate the red and blue channels at green CFA positions
for (int row = rcdBorder; row < tileRows - rcdBorder; row++) { for (int row = 4; row < tileRows - 4; ++row) {
for (int col = rcdBorder + (fc(cfarray, row, rcdBorder - 1) & 1), indx = row * tileSize + col; col < tilecols - rcdBorder; col += 2, indx += 2) { 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 // Refined vertical and horizontal local discrimination
float VH_Central_Value = VH_Dir[indx]; float VH_Central_Value = VH_Dir[indx];
@ -323,8 +313,13 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
} }
} }
for (int row = rowStart + rcdBorder; row < rowEnd - rcdBorder; ++row) { // For the outermost tiles in all directions we can use a smaller border margin
for (int col = colStart + rcdBorder; col < colEnd - rcdBorder; ++col) { const int last_vertical = rowEnd - ((tr == numTh - 1) ? rcdBorder : tileBorder);
const int first_horizontal = colStart + ((tc == 0) ? rcdBorder : tileBorder);
const int last_horizontal = colEnd - ((tc == numTw - 1) ? rcdBorder : tileBorder);
for(int row = rowStart + ((tr == 0) ? rcdBorder : tileBorder); row < last_vertical; row++) {
// for (int row = rowStart + tileBorder; row < rowEnd - tileBorder; ++row) {
for (int col = first_horizontal; col < last_horizontal; ++col) {
int idx = (row - rowStart) * tileSize + col - colStart ; int idx = (row - rowStart) * tileSize + col - colStart ;
red[row][col] = std::max(0.f, rgb[0][idx] * scale); red[row][col] = std::max(0.f, rgb[0][idx] * scale);
green[row][col] = std::max(0.f, rgb[1][idx] * scale); green[row][col] = std::max(0.f, rgb[1][idx] * scale);
@ -352,6 +347,8 @@ void RawImageSource::rcd_demosaic(size_t chunkSize, bool measure)
free(rgb); free(rgb);
free(VH_Dir); free(VH_Dir);
free(PQ_Dir); free(PQ_Dir);
free(P_CDiff_Hpf);
free(Q_CDiff_Hpf);
} }
border_interpolate(W, H, rcdBorder, rawData, red, green, blue); border_interpolate(W, H, rcdBorder, rawData, red, green, blue);