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Demosaicing.cpp
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Demosaicing.cpp
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#include <cstdio>
#include <iostream>
#include <fstream>
#include <cstring>
#include <vector>
#include <opencv2/opencv.hpp>
#include "Demosaicing.hpp"
using namespace std;
using namespace cv;
/* Type look Up
https://stackoverflow.com/questions/10167534/how-to-find-out-what-type-of-a-mat-object-is-with-mattype-in-opencv
+--------+----+----+----+----+------+------+------+------+
| | C1 | C2 | C3 | C4 | C(5) | C(6) | C(7) | C(8) |
+--------+----+----+----+----+------+------+------+------+
| CV_8U | 0 | 8 | 16 | 24 | 32 | 40 | 48 | 56 |
| CV_8S | 1 | 9 | 17 | 25 | 33 | 41 | 49 | 57 |
| CV_16U | 2 | 10 | 18 | 26 | 34 | 42 | 50 | 58 |
| CV_16S | 3 | 11 | 19 | 27 | 35 | 43 | 51 | 59 |
| CV_32S | 4 | 12 | 20 | 28 | 36 | 44 | 52 | 60 |
| CV_32F | 5 | 13 | 21 | 29 | 37 | 45 | 53 | 61 |
| CV_64F | 6 | 14 | 22 | 30 | 38 | 46 | 54 | 62 |
+--------+----+----+----+----+------+------+------+------+
*/
// Split 1 channel image into 3 channels according to bayer pattern
// R G
// G B
//
// m*n*1 -> m*n*3
void bayer_split(cv::Mat &Bayer,cv::Mat &Dst){
if(Bayer.channels() != 1){
std::cerr << "bayer_split allow only 1 channel raw bayer image " << std::endl;
return;
}
Dst = cv::Mat::zeros(Bayer.rows, Bayer.cols, CV_8UC3);
//cout << "SIZE: " << Dst.size() << " Channels: " << Dst.channels() << endl;
int channelNum;
for(int row = 0; row < Bayer.rows; row++){
for(int col = 0; col < Bayer.cols; col++){
if(row % 2 == 0){ // opencv: BGR
//even rows and even cols = R = channel:2
//even rows and odd cols = G = channel:1
channelNum = (col % 2 == 0) ? 2 : 1;
}else{
//odd rows and even cols = G = channel:1
//odd rows and odd cols = B = channel:0
channelNum = (col % 2 == 0) ? 1 : 0;
}
Dst.at<Vec3b>(row, col).val[channelNum] = Bayer.at<uchar>(row, col);
}
}
return;
}
// using bayer_split to get bayer mask according to bayer pattern
// R G
// G B
// val = 1 denote existing
void bayer_mask(cv::Mat &Bayer,cv::Mat &Dst){
Mat temp = cv::Mat::ones(Bayer.size(), CV_8U);
bayer_split(temp, Dst);
}
// Downsampling to 3 Channel Bayer according to `RGGB` pattern
// R G
// G B
//
void ConvertToThreeChannelBayerBG(Mat &BGRImage){
if(BGRImage.channels() != 3){
std::cerr << "ConvertToThreeChannelBayerBG allow only 3 channel bayer rgb image " << std::endl;
return;
}
Mat BayerImage(BGRImage.rows, BGRImage.cols, CV_8UC1);
int channel;
for (int row = 0; row < BGRImage.rows; row++){
for (int col = 0; col < BGRImage.cols; col++){
if (row % 2 == 0){
//even columns and even rows = red
//even columns and odd rows = green
channel = (col % 2 == 0) ? 0 : 1;
}else{
//odd columns and even rows = green
//odd columns and odd rows = blue
channel = (col % 2 == 0) ? 1 : 2;
}
for(int i = 0; i < 3; i++){
if(channel == i){
continue;
}
BGRImage.at<Vec3b>(row, col)[i] = 0;
}
}
}
}
// Downsampling to 1 Channel Bayer according to `RGGB` pattern
// R G
// G B
//
void toSingleChannel(cv::Mat &src,cv::Mat &dst){ //checked
if(src.channels() != 3){
std::cerr << "to_SingleChannel need 3 channel image" << std::endl;
return;
}
/*
dst = cv::Mat::zeros(src.rows, src.cols, CV_8UC1);
int channelNum;
for(int row = 0; row < src.rows; row++){
for(int col = 0; col < src.cols; col++){
if(row % 2 == 0){ // opencv: BGR
//even rows and even cols = R = channel:2
//even rows and odd cols = G = channel:1
channelNum = (col % 2 == 0) ? 2 : 1;
}else{
//odd rows and even cols = G = channel:1
//odd rows and odd cols = B = channel:0
channelNum = (col % 2 == 0) ? 1 : 0;
}
dst.at<uchar>(row, col) = src.at<Vec3b>(row, col).val[channelNum];
}
}*/
// faster way
// https://answers.opencv.org/question/3120/how-to-sum-a-3-channel-matrix-to-a-one-channel-matrix/
//https://docs.opencv.org/2.4/modules/core/doc/operations_on_arrays.html?highlight=transform#transform
transform(src, dst, cv::Matx13f(1,1,1));
return;
}
/* ==================== Smooth-hue Interpolation ====================
# https://patents.google.com/patent/US4642678A/en
# https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1207407
# http://www.sfu.ca/~gchapman/e895/e895l11.pdf
#
# The algorithm
# - interpolate `G`
# - compute hue for `R`,`B` channels at subsampled locations
# - interpolate hue for all pixels in `R`,`B` channels
# - determine chrominance `R`,`B` from hue
*/
void demosaic_smooth_hue(cv::Mat &Bayer,cv::Mat &Dst){
cv::Mat Src = Bayer.clone();
if(Bayer.channels() == 1){ //input 1 channel -> 3 channel Bayer
bayer_split(Bayer, Src);
}
//kernels
Mat K_G =( Mat_<float>(3,3) << 0, 1, 0, 1, 4, 1, 0, 1, 0);
K_G *= (1.0/4.0);
Mat K_B = (Mat_<float>(3,3) << 1, 2, 1, 2, 4, 2, 1, 2, 1);
K_B *= (1.0/4.0);
Mat K_R = K_B;
//split channel to BGR
Src.convertTo(Src, CV_32F, 1.0 / 255.0); //to float
vector<Mat> bgr(3);
split(Src, bgr);
// interpolate luminance G
filter2D(bgr[1], bgr[1], CV_32F, K_G);
// compute hue (B-G), (R-G)
// R G
// G B
for(int row = 0; row < bgr[1].rows; row++){
int col = 0;
if(row % 2 == 1){
col = 1;
}
for(; col < bgr[1].cols; col += 2){
if(row % 2 == 0){ //red
bgr[2].at<float>(row, col) -= bgr[1].at<float>(row, col);
}else{ //blue
bgr[0].at<float>(row, col) -= bgr[1].at<float>(row, col);
}
}
}
// interpolate hue
filter2D(bgr[0], bgr[0], CV_32F, K_B);
filter2D(bgr[2], bgr[2], CV_32F, K_R);
// Compute chrominance B,R
bgr[0] = bgr[0] + bgr[1];
bgr[2] = bgr[2] + bgr[1];
merge(bgr, Dst);
Dst.convertTo(Dst, CV_8U, 255.0);
return;
}
/* ==================== Laplacian-corrected linear filter (MATLAB's demosaic) ====================
# https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1326587
# https://www.ipol.im/pub/art/2011/g_mhcd/article.pdf
*/
void demosaic_laplacian_corrected(cv::Mat &Bayer,cv::Mat &Dst, float alpha = 1.0/2, float beta = 5.0/8, float gamma = 3.0/4){
cv::Mat Src = Bayer.clone();
if(Bayer.channels() == 1){ //input 1 channel -> 3 channel Bayer
bayer_split(Bayer, Src);
}
// kernels
Mat K_L = ( Mat_<float>(5,5) <<
0, 0, -1, 0, 0,
0, 0, 0, 0, 0,
-1, 0, 4, 0, -1,
0, 0, 0, 0, 0,
0, 0, -1, 0, 0);
K_L *= (1.0/4.0);
Mat K_G =( Mat_<float>(3,3) << 0, 1, 0, 1, 4, 1, 0, 1, 0);
K_G *= (1.0/4.0);
Mat K_B = (Mat_<float>(3,3) << 1, 2, 1, 2, 4, 2, 1, 2, 1);
K_B *= (1.0/4.0);
Mat K_R = K_B;
// split channel to BGR
Src.convertTo(Src, CV_32F, 1.0 / 255.0); //to float
vector<Mat> bgr(3);
vector<Mat> laplacian(3);
split(Src, bgr);
split(Src, laplacian);
// interpolate luminance R, G, B
filter2D(bgr[0], bgr[0], CV_32F, K_B);
filter2D(bgr[1], bgr[1], CV_32F, K_G);
filter2D(bgr[2], bgr[2], CV_32F, K_R);
// Compute discrete laplacian in 5x5 neighborhood
filter2D(laplacian[0], laplacian[0], CV_32F, K_L);
filter2D(laplacian[1], laplacian[1], CV_32F, K_L);
filter2D(laplacian[2], laplacian[2], CV_32F, K_L);
// Laplacian correction
// R G
// G B
for(int row = 0; row < Bayer.rows; row++){
for(int col = 0; col < Bayer.cols; col++){
if(row % 2 == 0 && col % 2 == 0){ //Red
//Blue @ Red
bgr[0].at<float>(row, col) += gamma * laplacian[2].at<float>(row, col);
//Green @ Red
bgr[1].at<float>(row, col) += alpha * laplacian[2].at<float>(row, col);
}else if(row % 2 == 1 && col % 2 == 1){ //Blue
//Green @ Blue
bgr[1].at<float>(row, col) += alpha * laplacian[0].at<float>(row, col);
//Red @ Blue
bgr[2].at<float>(row, col) += beta * laplacian[0].at<float>(row, col);
}else{
//Red @ Green
bgr[2].at<float>(row, col) += beta * laplacian[1].at<float>(row, col);
//Blue @ Green
bgr[0].at<float>(row, col) += gamma * laplacian[1].at<float>(row, col);
}
}
}
merge(bgr, Dst);
Dst.convertTo(Dst, CV_8U, 255.0);
return;
}
/* ==================== Gradient based threshold free ====================
# https://ieeexplore.ieee.org/document/5654327 (2010)
*/
void demosaic_GBTF(cv::Mat &Bayer,cv::Mat &Dst){
cv::Mat Src = Bayer.clone();
if(Bayer.channels() == 1){ //input 1 channel -> 3 channel Bayer
bayer_split(Bayer, Src);
}
// split channel to BGR
Src.convertTo(Src, CV_32F, 1.0 / 255.0); //to float
vector<Mat> bgr(3);
vector<Mat> finalBGR(3);
split(Src, bgr);
split(Src, finalBGR);
// 2.1. Green Channel Interpolation
// Hamilton and Adams’ interpolation for B', G', R'
float G_H, G_V;
float R_H, R_V;
float B_H, B_V;
// for interpolation purpose
copyMakeBorder(bgr[0], bgr[0], 2, 2, 2, 2, cv::BORDER_DEFAULT);
copyMakeBorder(bgr[1], bgr[1], 2, 2, 2, 2, cv::BORDER_DEFAULT);
copyMakeBorder(bgr[2], bgr[2], 2, 2, 2, 2, cv::BORDER_DEFAULT);
// horizontal and vertical color difference
Mat V_Diff(Src.size(), CV_32F, cv::Scalar(0));
Mat H_Diff(Src.size(), CV_32F, cv::Scalar(0));
// [Bayer] [Horizontal] [Vertical]
// R G R G D_gr D_gr D_gr D_gr D_gr D_gb D_gr D_gb
// G B G B -> D_gb D_gb D_gb D_gb & D_gr D_gb D_gr D_gb
// R G R G D_gr D_gr D_gr D_gr D_gr D_gb D_gr D_gb
// G B G B D_gb D_gb D_gb D_gb D_gr D_gb D_gr D_gb
for(int row = 0; row < Src.rows; row++){
for(int col = 0; col < Src.cols; col++){
int i = row + 2;
int j = col + 2;
if(row % 2 == 0 && col % 2 == 0){ //Red
G_H = (bgr[1].at<float>(i, j - 1) + bgr[1].at<float>(i, j + 1)) * 0.5
+ (2 * bgr[2].at<float>(i, j) - bgr[2].at<float>(i, j - 2) - bgr[2].at<float>(i, j + 2)) * 0.25;
G_V = (bgr[1].at<float>(i - 1, j) + bgr[1].at<float>(i + 1, j)) * 0.5
+ (2 * bgr[2].at<float>(i, j) - bgr[2].at<float>(i - 2, j) - bgr[2].at<float>(i + 2, j)) * 0.25;
// cal G' - R difference
V_Diff.at<float>(row, col) = G_V - bgr[2].at<float>(i, j);
H_Diff.at<float>(row, col) = G_H - bgr[2].at<float>(i, j);
}else if(row % 2 == 1 && col % 2 == 1){ //Blue
G_H = (bgr[1].at<float>(i, j - 1) + bgr[1].at<float>(i, j + 1)) * 0.5
+ (2 * bgr[0].at<float>(i, j) - bgr[0].at<float>(i, j - 2) - bgr[0].at<float>(i, j + 2)) * 0.25;
G_V = (bgr[1].at<float>(i - 1, j) + bgr[1].at<float>(i + 1, j)) * 0.5
+ (2 * bgr[0].at<float>(i, j) - bgr[0].at<float>(i - 2, j) - bgr[0].at<float>(i + 2, j)) * 0.25;
// cal G' - B difference
V_Diff.at<float>(row, col) = G_V - bgr[0].at<float>(i, j);
H_Diff.at<float>(row, col) = G_H - bgr[0].at<float>(i, j);
}else if(row % 2 == 1 && col % 2 == 0){ //Green with Red Vertical / Blue Horizontal
R_V = (bgr[2].at<float>(i - 1, j) + bgr[2].at<float>(i + 1, j)) * 0.5
+ (2 * bgr[1].at<float>(i, j) - bgr[1].at<float>(i - 2, j) - bgr[1].at<float>(i + 2, j)) * 0.25;
B_H = (bgr[0].at<float>(i, j - 1) + bgr[0].at<float>(i, j + 1)) * 0.5
+ (2 * bgr[1].at<float>(i, j) - bgr[1].at<float>(i, j - 2) - bgr[1].at<float>(i, j + 2)) * 0.25;
// cal G - R', G - B' difference
V_Diff.at<float>(row, col) = bgr[1].at<float>(i, j) - R_V;
H_Diff.at<float>(row, col) = bgr[1].at<float>(i, j) - B_H;
}else{ //Green with Red Horizontal / Blue Vertical
R_H = (bgr[2].at<float>(i, j - 1) + bgr[2].at<float>(i, j + 1)) * 0.5
+ (2 * bgr[1].at<float>(i, j) - bgr[1].at<float>(i, j - 2) - bgr[1].at<float>(i, j + 2)) * 0.25;
B_V = (bgr[0].at<float>(i - 1, j) + bgr[0].at<float>(i + 1, j)) * 0.5
+ (2 * bgr[1].at<float>(i, j) - bgr[1].at<float>(i - 2, j) - bgr[1].at<float>(i + 2, j)) * 0.25;
// cal G - R', G - B' difference
H_Diff.at<float>(row, col) = bgr[1].at<float>(i, j) - R_H;
V_Diff.at<float>(row, col) = bgr[1].at<float>(i, j) - B_V;
}
}
}
// Final difference estimation for the target pixel
copyMakeBorder(V_Diff, V_Diff, 4, 4, 4, 4, cv::BORDER_DEFAULT); // now V_Diff need shift row and col by 4
copyMakeBorder(H_Diff, H_Diff, 4, 4, 4, 4, cv::BORDER_DEFAULT);
Mat gr_Diff(Src.size(), CV_32F, cv::Scalar(0));
Mat gb_Diff(Src.size(), CV_32F, cv::Scalar(0));
// use gradients of color differences to come up with weights for each direction.
Mat V_diff_gradient;
Mat H_diff_gradient;
float VHkernel[3] = {-1,0,1}; //(central difference form)
cv::Mat HK(1, 3, CV_32F, VHkernel);
cv::Mat VK(3, 1, CV_32F, VHkernel);
cv::filter2D(V_Diff, V_diff_gradient, -1, VK);
cv::filter2D(H_Diff, H_diff_gradient, -1, HK);
V_diff_gradient = cv::abs(V_diff_gradient); // V_diff_gradient need shift row and col by 4
H_diff_gradient = cv::abs(H_diff_gradient);
float Weight[4]; //four direction, N, S, W, E
int startPoint[4][2] = {{-4, -2}, {0, -2}, {-2, -4}, {-2, 0}}; //weight startpoint
float W_total;
int a, b;
for(int row = 0; row < Src.rows; row++){
int col = 0;
if(row % 2 == 1){
col = 1;
}
for(; col < Src.cols; col += 2){
// calculate Weight
Weight[0] = Weight[1] = Weight[2] = Weight[3] = W_total = 0.0;
for(int dir = 0; dir < 4; dir++){// N, S, W, E
a = startPoint[ dir ][0];
b = startPoint[ dir ][1];
for(int i = 0; i < 5; i++){
for(int j = 0; j < 5; j++){
if(dir < 2){ // N, S -> Vertical
Weight[ dir ] += V_diff_gradient.at<float>(4 + row + a + i, 4 + col + b + j); // shift 4 due to copyMakeBorder
}else{ // W, E -> Horizontal
Weight[ dir ] += H_diff_gradient.at<float>(4 + row + a + i, 4 + col + b + j);
}
}
}
Weight[ dir ] *= Weight[ dir ] ;
Weight[ dir ] = 1.0/Weight[ dir ];
W_total += Weight[ dir ];
}
// calculate gr_Diff/gb_Diff & finalGreen
int i = row + 4;
int j = col + 4;
if(row % 2 == 0){ //Green @ Red
gr_Diff.at<float>(row, col) =
( Weight[0] * 0.2 * (V_Diff.at<float>(i - 4, j) + V_Diff.at<float>(i - 3, j) + V_Diff.at<float>(i - 2, j) + V_Diff.at<float>(i - 1, j) + V_Diff.at<float>(i, j))
+ Weight[1] * 0.2 * (V_Diff.at<float>(i, j) + V_Diff.at<float>(i + 1, j) + V_Diff.at<float>(i + 2, j) + V_Diff.at<float>(i + 3, j) + V_Diff.at<float>(i + 4, j))
+ Weight[2] * 0.2 * (H_Diff.at<float>(i, j - 4) + H_Diff.at<float>(i, j - 3) + H_Diff.at<float>(i, j - 2) + H_Diff.at<float>(i, j - 1) + H_Diff.at<float>(i, j))
+ Weight[3] * 0.2 * (H_Diff.at<float>(i, j) + H_Diff.at<float>(i, j + 1) + H_Diff.at<float>(i, j + 2) + H_Diff.at<float>(i, j + 3) + H_Diff.at<float>(i, j + 4))
) / W_total;
finalBGR[1].at<float>(row, col) = finalBGR[2].at<float>(row, col) + gr_Diff.at<float>(row, col); // R + gb_Diff
}else{ //Green @ Blue
gb_Diff.at<float>(row, col) =
( Weight[0] * 0.2 * (V_Diff.at<float>(i - 4, j) + V_Diff.at<float>(i - 3, j) + V_Diff.at<float>(i - 2, j) + V_Diff.at<float>(i - 1, j) + V_Diff.at<float>(i, j))
+ Weight[1] * 0.2 * (V_Diff.at<float>(i, j) + V_Diff.at<float>(i + 1, j) + V_Diff.at<float>(i + 2, j) + V_Diff.at<float>(i + 3, j) + V_Diff.at<float>(i + 4, j))
+ Weight[2] * 0.2 * (H_Diff.at<float>(i, j - 4) + H_Diff.at<float>(i, j - 3) + H_Diff.at<float>(i, j - 2) + H_Diff.at<float>(i, j - 1) + H_Diff.at<float>(i, j))
+ Weight[3] * 0.2 * (H_Diff.at<float>(i, j) + H_Diff.at<float>(i, j + 1) + H_Diff.at<float>(i, j + 2) + H_Diff.at<float>(i, j + 3) + H_Diff.at<float>(i, j + 4))
) / W_total;
finalBGR[1].at<float>(row, col) = finalBGR[0].at<float>(row, col) + gb_Diff.at<float>(row, col); // B + gb_Diff
}
}
}
// 2.2. Red and Blue Channel Interpolation
float PrbData[49] = {
0, 0, -0.03125, 0, -0.03125, 0, 0,
0,0,0,0,0,0,0,
-0.03125,0,0.3125,0,0.3125,0,-0.03125,
0,0,0,0,0,0,0,
-0.03125,0,0.3125,0,0.3125,0,-0.03125,
0,0,0,0,0,0,0,
0,0,-0.03125,0,-0.03125,0,0
};
cv::Mat Prb(7, 7, CV_32FC1, PrbData);
copyMakeBorder(gr_Diff, gr_Diff, 3, 3, 3, 3, cv::BORDER_DEFAULT);
copyMakeBorder(gb_Diff, gb_Diff, 3, 3, 3, 3, cv::BORDER_DEFAULT);
// Red pixel values at blue locations and blue pixel values at redlocations
// R G
// G B
for(int row = 0; row < Bayer.rows; row++){
int col = 0;
if(row % 2 == 1){
col = 1;
}
//https://stackoverflow.com/questions/21874774/sum-of-elements-in-a-matrix-in-opencv
for(; col < Bayer.cols; col += 2){
if(row % 2 == 0){ //Red
//Blue @ Red
finalBGR[0].at<float>(row, col) = finalBGR[1].at<float>(row, col) - cv::sum( gb_Diff(cv::Range(3 + row - 3, 3 + row + 3 + 1), cv::Range( 3 + col - 3, 3 + col + 3 + 1)).mul(Prb) )[0];
}else{ //Blue
//Red @ Blue
finalBGR[2].at<float>(row, col) = finalBGR[1].at<float>(row, col) - cv::sum( gr_Diff(cv::Range(3 + row - 3, 3 + row + 3 + 1), cv::Range( 3 + col - 3, 3 + col + 3 + 1)).mul(Prb) )[0];
}
}
}
// For red and blue pixels at green locations, we use bilinearinterpolation over the closest four neighbors
// R G
// G B
copyMakeBorder(finalBGR[0], bgr[0], 1, 1, 1, 1, cv::BORDER_DEFAULT);
copyMakeBorder(finalBGR[1], bgr[1], 1, 1, 1, 1, cv::BORDER_DEFAULT);
copyMakeBorder(finalBGR[2], bgr[2], 1, 1, 1, 1, cv::BORDER_DEFAULT);
for(int row = 0; row < Src.rows; row++){
int col = 0;
if(row % 2 == 0){
col = 1;
}
for(; col < Src.cols; col += 2){ //Green
int i = row + 1;
int j = col + 1;
// Red
finalBGR[2].at<float>(row, col) = finalBGR[1].at<float>(row, col)
- (bgr[1].at<float>(i - 1, j) - bgr[2].at<float>(i - 1, j)) / 4.0
- (bgr[1].at<float>(i + 1, j) - bgr[2].at<float>(i + 1, j)) / 4.0
- (bgr[1].at<float>(i, j - 1) - bgr[2].at<float>(i, j - 1)) / 4.0
- (bgr[1].at<float>(i, j + 1) - bgr[2].at<float>(i, j + 1)) / 4.0;
// Blue
finalBGR[0].at<float>(row, col) = finalBGR[1].at<float>(row, col)
- (bgr[1].at<float>(i - 1, j) - bgr[0].at<float>(i - 1, j)) / 4.0
- (bgr[1].at<float>(i + 1, j) - bgr[0].at<float>(i + 1, j)) / 4.0
- (bgr[1].at<float>(i, j - 1) - bgr[0].at<float>(i, j - 1)) / 4.0
- (bgr[1].at<float>(i, j + 1) - bgr[0].at<float>(i, j + 1)) / 4.0;
}
}
merge(finalBGR, Dst);
Dst.convertTo(Dst, CV_8U, 255.0);
return;
}
// =============== Guided Filter ===================
// https://medium.com/@gary1346aa/%E5%B0%8E%E5%90%91%E6%BF%BE%E6%B3%A2%E7%9A%84%E5%8E%9F%E7%90%86%E4%BB%A5%E5%8F%8A%E5%85%B6%E6%87%89%E7%94%A8-78fdf562e749
// https://github.com/atilimcetin/guided-filter
// I: imput
// r: (radius)
Mat box_filter(const Mat &I, int r){
Mat result;
// = call boxFiltr (all average)
// boxFilter(src, dst, src.type(), anchor, true, borderType).
blur(I, result, Size(2 * r + 1, 2 * r + 1)); //create Mat data by cv function so can return (not user allocated data)
return result;
}
// p: origin img (assume CV_8U or 0~255)
// I: guided img
// r: local window radius
// eps: regularization parameter (0.1)^2, (0.2)^2...
Mat guided_filter(const Mat &originP, const Mat &originI, int r = 2, float eps=0.0){
Mat p, I;
originP.convertTo(p, CV_32F, 1.0 / 255.0); //(a * (i,j) + b)
originI.convertTo(I, CV_32F, 1.0 / 255.0);
// step: 1
Mat mean_I = box_filter(I, r);
Mat mean_p = box_filter(p, r);
Mat corr_I = box_filter(I.mul(I), r); //mul: element wise mul
Mat corr_Ip = box_filter(I.mul(p), r);
// step: 2
Mat var_I = corr_I - mean_I.mul(mean_I);
Mat cov_Ip = corr_Ip - mean_I.mul(mean_p);
// step: 3
Mat a;
if (var_I.channels() == 3){
a = cov_Ip / (var_I + Scalar(eps, eps, eps)); //otherwise only 1 channel get added
}else{
a = cov_Ip / (var_I + eps);
}
Mat b = mean_p - a.mul(mean_I);
// step: 4
Mat mean_a = box_filter(a, r);
Mat mean_b = box_filter(b, r);
// step: 5
Mat q = mean_a.mul(I) + mean_b;
Mat res;
q.convertTo(q, CV_8U, 255.0);
return q;
}
// =============== Guided Filter Modified ===================
// I: imput
// h, v: local window radius
Mat box_filter_modified(const Mat &I, int h, int v){
Mat result;
// = call boxFiltr (all average)
// boxFilter(src, dst, src.type(), anchor, true, borderType).
blur(I, result, Size(2 * h + 1, 2 * v + 1)); //width * height
return result * (2 * h + 1) * (2 * v + 1);
}
// p: origin img (assume float or CV_32F, 0.0 ~ 1.0)
// I: guided img
// M: binary mask
// h, v: local window radius
// eps: regularization parameter (0.1)^2, (0.2)^2...
Mat guided_filter_modified(const Mat &originP, const Mat &originI, const Mat &M, int h = 2, int v = 2, float eps=0.0){
Mat p, I;
p = originP.clone();
I = originI.clone();
// seems need mul(M) for all I if I will not be zero outside mask
//The number of the sammpled pixels in each local patch
Mat N = box_filter_modified(M, h, v);
Mat temp = (N == 0);
temp.convertTo(temp, CV_32F, 1.0/255.0);
N = N + temp;
// The size of each local patch; N=(2h+1)*(2v+1) except for boundary pixels.
Mat N2 = box_filter_modified(Mat::ones(I.rows, I.cols, CV_32F), h, v);
// step: 1
Mat mean_I = box_filter_modified(I.mul(M), h, v);
divide(mean_I, N, mean_I);
Mat mean_p = box_filter_modified(p, h, v);
divide(mean_p, N, mean_p);
Mat corr_I = box_filter_modified(I.mul(I).mul(M), h, v); //mul: element wise mul
divide(corr_I, N, corr_I);
Mat corr_Ip = box_filter_modified(I.mul(p), h, v);
divide(corr_Ip, N, corr_Ip);
// step: 2
Mat var_I = corr_I - mean_I.mul(mean_I);
//threshold parameter
float th = 0.00001;
for(int row = 0; row < var_I.rows; row++){
float *p = var_I.ptr<float>(row);
for(int col = 0; col < var_I.cols; col++){
if(p[col] < th){
p[col] = th;
}
}
}
Mat cov_Ip = corr_Ip - mean_I.mul(mean_p);
// step: 3
Mat a;
if (var_I.channels() == 3){
a = cov_Ip / (var_I + Scalar(eps, eps, eps)); //otherwise only 1 channel get added
}else{
a = cov_Ip / (var_I + eps);
}
Mat b = mean_p - a.mul(mean_I);
// step: 4
Mat mean_a = box_filter_modified(a, h, v);
divide(mean_a, N2, mean_a);
Mat mean_b = box_filter_modified(b, h, v);
divide(mean_b, N2, mean_b);
// step: 5
Mat q = mean_a.mul(I) + mean_b;
Mat res;
//q.convertTo(q, CV_8U, 255.0);
return q;
}
// =============== Residual Interpolation ===================
// http://www.ok.sc.e.titech.ac.jp/res/DM/RI.pdf (2013)
// sigma: standard deviation of gaussian filter
void demosaic_residual(cv::Mat &Bayer,cv::Mat &Dst, float sigma = 1.0){
cv::Mat Src = Bayer.clone();
if(Bayer.channels() == 1){ //input 1 channel -> 3 channel Bayer
bayer_split(Bayer, Src);
}
Mat Src1ch;
toSingleChannel(Src, Src1ch); //cv8U
Src1ch.convertTo(Src1ch, CV_32F, 1.0 / 255.0); //normalize
Mat tempMask;
bayer_mask(Src, tempMask);
tempMask.convertTo(tempMask, CV_32F); //normalize
vector<Mat> mask(3);
split(tempMask, mask);
tempMask.release();
// split channel to BGR
Src.convertTo(Src, CV_32F, 1.0 / 255.0); //normalize
vector<Mat> bgr(3);
vector<Mat> finalBGR(3);
split(Src, bgr);
split(Src, finalBGR);
// ==== 1.Green interpolation ===
// get green mask, care it depend on bayer type
// R G -> 0 1 -> 0 0
// G B 0 0 1 0
// see horizontal: Gr or Gb
Mat maskGr = Mat::zeros(Src.rows, Src.cols, CV_32F);
Mat maskGb = Mat::zeros(Src.rows, Src.cols, CV_32F);
for(int row = 0; row < Src.rows; row++){
int col = 0;
float *targetGr = maskGr.ptr<float>(row);
float *targetGb = maskGb.ptr<float>(row);
if(row % 2 == 0){
col = 1;
}
for(; col < Src.cols; col += 2){
if(row % 2 == 0){ // R G
targetGr[col] = 1.0;
}else{ //G B
targetGb[col] = 1.0;
}
}
}
float VHkernel[3] = {0.5, 0, 0.5}; //bilinear interpolation at 1D
cv::Mat HK(1, 3, CV_32F, VHkernel);
cv::Mat VK(3, 1, CV_32F, VHkernel);
Mat rawH, rawV;
filter2D(Src1ch, rawH, -1, HK); //original matlab all using 'replicate' for filter
filter2D(Src1ch, rawV, -1, VK);
// Guide image
Mat GuideG_H = bgr[1] + rawH.mul(mask[2]) + rawH.mul(mask[0]);//Gh @ R: 1/2(Gh left + Gh right), Gh @ B: 1/2(Gh left + Gh right)
Mat GuideR_H = bgr[2] + rawH.mul(maskGr); //R pixel + r nearby rawH(= red bilinear part)
Mat GuideB_H = bgr[0] + rawH.mul(maskGb); //B pixel + b nearby rawH(= blue bilinear part)
Mat GuideG_V = bgr[1] + rawV.mul(mask[2]) + rawV.mul(mask[0]);//bilinear interpolation
Mat GuideR_V = bgr[2] + rawV.mul(maskGb); //vertical need change Gr <-> Gb
Mat GuideB_V = bgr[0] + rawV.mul(maskGr);
// Tentative image
int h = 5; //horizontal
int v = 0; //vertical
float eps = 0;
Mat tentativeR_H = guided_filter_modified(bgr[2], GuideG_H, mask[2], h, v, eps);
Mat tentativeGr_H = guided_filter_modified(bgr[1].mul(maskGr), GuideR_H, maskGr, h, v, eps);// need mul(mask) because Green has two location
Mat tentativeGb_H = guided_filter_modified(bgr[1].mul(maskGb), GuideB_H, maskGb, h, v, eps);
Mat tentativeB_H = guided_filter_modified(bgr[0], GuideG_H, mask[0], h, v, eps);
// vertical part
Mat tentativeR_V = guided_filter_modified(bgr[2], GuideG_V, mask[2], v, h, eps);
Mat tentativeGr_V = guided_filter_modified(bgr[1].mul(maskGb), GuideR_V, maskGb, v, h, eps); //Gr <-> Gb
Mat tentativeGb_V = guided_filter_modified(bgr[1].mul(maskGr), GuideB_V, maskGr, v, h, eps);
Mat tentativeB_V = guided_filter_modified(bgr[0], GuideG_V, mask[0], v, h, eps);
//relsease Guided Image
GuideG_H.release();
GuideR_H.release();
GuideB_H.release();
GuideG_V.release();
GuideR_V.release();
GuideB_V.release();
// Residual
Mat residualGr_H = (bgr[1] - tentativeGr_H).mul(maskGr);
Mat residualGb_H = (bgr[1] - tentativeGb_H).mul(maskGb);
Mat residualR_H = (bgr[2] - tentativeR_H).mul(mask[2]);
Mat residualB_H = (bgr[0] - tentativeB_H).mul(mask[0]);
Mat residualGr_V = (bgr[1] - tentativeGr_V).mul(maskGb);
Mat residualGb_V = (bgr[1] - tentativeGb_V).mul(maskGr);
Mat residualR_V = (bgr[2] - tentativeR_V).mul(mask[2]);
Mat residualB_V = (bgr[0] - tentativeB_V).mul(mask[0]);
// Residual interpolation
filter2D(residualGr_H, residualGr_H, -1, HK); //original matlab using 'replicate'
filter2D(residualGb_H, residualGb_H, -1, HK);
filter2D(residualR_H, residualR_H, -1, HK);
filter2D(residualB_H, residualB_H, -1, HK);
// verical part
filter2D(residualGr_V, residualGr_V, -1, VK);
filter2D(residualGb_V, residualGb_V, -1, VK);
filter2D(residualR_V, residualR_V, -1, VK);
filter2D(residualB_V, residualB_V, -1, VK);
// Add tentative image
Mat Gr_H = ( tentativeGr_H + residualGr_H ).mul(mask[2]);
Mat Gb_H = ( tentativeGb_H + residualGb_H ).mul(mask[0]);
Mat R_H = ( tentativeR_H + residualR_H ).mul(maskGr);
Mat B_H = ( tentativeB_H + residualB_H ).mul(maskGb);
Mat Gr_V = ( tentativeGr_V + residualGr_V ).mul(mask[2]);
Mat Gb_V = ( tentativeGb_V + residualGb_V ).mul(mask[0]);
Mat R_V = ( tentativeR_V + residualR_V ).mul(maskGb);
Mat B_V = ( tentativeB_V + residualB_V ).mul(maskGr);
// Vertical and horizontal color difference
Mat dif_H = bgr[1] + Gr_H + Gb_H - bgr[2] - bgr[0] - R_H - B_H;
Mat dif_V = bgr[1] + Gr_V + Gb_V - bgr[2] - bgr[0] - R_V - B_V;
//release not used Mat for memory useage
tentativeR_H.release();
tentativeGr_H.release();
tentativeGb_H.release();
tentativeB_H.release();
tentativeR_V.release();
tentativeGr_V.release();
tentativeGb_V.release();
tentativeB_V.release();
residualGr_H.release();
residualGb_H.release();
residualR_H.release();
residualB_H.release();
residualGr_V.release();
residualGb_V.release();
residualR_V.release();
residualB_V.release();
Gr_H.release();
Gb_H.release();
R_H.release();
B_H.release();
Gr_V.release();
Gb_V.release();
R_V.release();
B_V.release();
// Combine Vertical and Horizontal Color Differences
// color difference gradient
float difkernel[3] = {1, 0, -1};
Mat dif_H_K(1, 3, CV_32F, difkernel);
Mat dif_V_K(3, 1, CV_32F, difkernel);
Mat V_diff_gradient, H_diff_gradient;
filter2D(dif_H, H_diff_gradient, -1, dif_H_K);
filter2D(dif_V, V_diff_gradient, -1, dif_V_K);
//filter2D(dif_H, H_diff_gradient, -1, dif_H_K, Point(-1,-1), 0.0, BORDER_REPLICATE );
//filter2D(dif_V, V_diff_gradient, -1, dif_V_K, Point(-1,-1), 0.0, BORDER_REPLICATE );
H_diff_gradient = cv::abs(H_diff_gradient);
V_diff_gradient = cv::abs(V_diff_gradient);
// Directional weight (four direction)
Mat K = Mat::ones(5, 5, CV_32F);
Mat hWeightSum, vWeightSum;
filter2D(H_diff_gradient, hWeightSum, -1, K); //Add up 5x5 weight patch first
filter2D(V_diff_gradient, vWeightSum, -1, K);
Mat Wkernel = (Mat_<float>(1, 5) << 1, 0, 0, 0, 0); //shift kernal
Mat Ekernel = (Mat_<float>(1, 5) << 0, 0, 0, 0, 1);
Mat Nkernel = (Mat_<float>(5, 1) << 1, 0, 0, 0, 0);
Mat Skernel = (Mat_<float>(5, 1) << 0, 0, 0, 0, 1);
Mat wWeight, eWeight, nWeight, sWeight;
filter2D(hWeightSum, wWeight, -1, Wkernel); //shift
filter2D(hWeightSum, eWeight, -1, Ekernel);
filter2D(vWeightSum, nWeight, -1, Nkernel);
filter2D(vWeightSum, sWeight, -1, Skernel);
divide(1.0, (wWeight.mul(wWeight) + 1e-32), wWeight); //divide(float scale, InputArray src2, OutputArray dst, int dtype=-1)
divide(1.0, (eWeight.mul(eWeight) + 1e-32), eWeight);
divide(1.0, (nWeight.mul(nWeight) + 1e-32), nWeight);
divide(1.0, (sWeight.mul(sWeight) + 1e-32), sWeight);
// combine directional color differences
Mat weightedF = getGaussianKernel(9, sigma, CV_32F); //(int ksize, float sigma, int ktype=CV_64F). return [ksize x 1] matrix
Nkernel = (Mat_<float>(9, 1) << 1, 1, 1, 1, 1, 0, 0, 0, 0);
Nkernel = Nkernel.mul(weightedF);
float s = sum(Nkernel)[0];
Nkernel /= s;
Skernel = (Mat_<float>(9, 1) << 0, 0, 0, 0, 1, 1, 1, 1, 1);
Skernel = Skernel.mul(weightedF) / s;
transpose(Nkernel, Wkernel);
transpose(Skernel, Ekernel);
Mat fMulGradientSum_N, fMulGradientSum_S, fMulGradientSum_W, fMulGradientSum_E;
filter2D(dif_V, fMulGradientSum_N, -1, Nkernel);
filter2D(dif_V, fMulGradientSum_S, -1, Skernel);
filter2D(dif_H, fMulGradientSum_W, -1, Wkernel);
filter2D(dif_H, fMulGradientSum_E, -1, Ekernel);
Mat totalWeight = nWeight + eWeight + wWeight + sWeight;
Mat diff;
divide(nWeight.mul(fMulGradientSum_N) + sWeight.mul(fMulGradientSum_S) + wWeight.mul(fMulGradientSum_W) + eWeight.mul(fMulGradientSum_E), totalWeight, diff);//(InputArray src1, InputArray src2, OutputArray dst, float scale=1, int dtype=-1)
// Calculate Green by adding bayer raw data
finalBGR[1] = diff + Src1ch; //raw CFA data
Mat imask = (mask[1] == 0); //[0, 1, 1] -> [255, 0, 0]
imask.convertTo(imask, CV_32F, 1.0/255.0);
finalBGR[1] = finalBGR[1].mul(imask) + bgr[1]; //avoid original green value been modified
// clip to 0~1
//https://docs.opencv.org/master/db/d8e/tutorial_threshold.html
//(InputArray src, OutputArray dst, float thresh, float maxval, int type)
threshold(finalBGR[1], finalBGR[1], 1.0, 1.0, THRESH_TRUNC); // > 1 to 1
threshold(finalBGR[1], finalBGR[1], 0.0, 0.0, THRESH_TOZERO); // < 0 to 0
//https://docs.opencv.org/master/d3/d63/classcv_1_1Mat.html#adf88c60c5b4980e05bb556080916978b
//although converTo() wil auto clamped to min/max
// === 2.Red and Blue ===
h = 5; //horizontal
v = 5; //vertical
eps = 0.0;
// R interpolation
Mat tentativeR = guided_filter_modified(bgr[2], finalBGR[1], mask[2], h, v, eps);
Mat residualR = (bgr[2] - tentativeR).mul(mask[2]);
Mat bilinearKernel = (Mat_<float>(3, 3) << 0.25, 0.5, 0.25, 0.5, 1.0, 0.5, 0.25, 0.5, 0.25);
filter2D(residualR, residualR, -1, bilinearKernel);
finalBGR[2] = residualR + tentativeR;
// B interpolation
Mat tentativeB = guided_filter_modified(bgr[0], finalBGR[1], mask[0], h, v, eps);
Mat residualB = (bgr[0] - tentativeB).mul(mask[0]);
filter2D(residualB, residualB, -1, bilinearKernel);
finalBGR[0] = residualB + tentativeB;
// Merge to single 3 channel Img
merge(finalBGR, Dst);
Dst.convertTo(Dst, CV_8U, 255.0);
}
void Demosaicing(std::string &BayerFileName, cv::Mat &Dst, int rows, int cols, int BayerPatternFlag, int DemosaicingMethod){
Mat RawImage;
Mat BayerImage;
RawImage.create(rows, cols, CV_16UC1); //16 bit short
//https://stackoverflow.com/questions/15138353/how-to-read-a-binary-file-into-a-vector-of-unsigned-chars
ifstream inFile;
inFile.open(BayerFileName, ios::binary);
if (!inFile.is_open()){
cout << "Unable to open file" << endl;
return;
}
//16 bit -> short
// Stop eating new lines in binary mode!!!
inFile.unsetf(std::ios::skipws);
// get its size:
std::streampos fileSize;
inFile.seekg(0, std::ios::end);
fileSize = inFile.tellg();
inFile.seekg(0, std::ios::beg);
//read data
inFile.read((char *)RawImage.data, rows * cols * sizeof(short)); //<-- 16 bit short
inFile.close();
//cout << "rows: " << RawImage.rows << endl;
//cout << "cols: " << RawImage.cols << endl;
//cout << "size: " << RawImage.size() << endl;
//cout << "dept: " << RawImage.depth() << endl; //0~4
//cout << "type: " << RawImage.type() << endl;
//cout << "chal: " << RawImage.channels() << endl
RawImage.convertTo(BayerImage, CV_32FC1);
BayerImage.convertTo(BayerImage, CV_8UC1, 255.0 / 1023.0); // divide depend on img bit, care opencv saturate_case
// R G R
// G B G
// R G R
//copyMakeBorder(src, dst, int top, int bottom, int left, int right, int borderType, const Scalar& value=Scalar() )
switch(BayerPatternFlag){ //turn all to RGGB by copyMakeBorder
case 1: //RGGB
break;
case 2: //GRBG
// R|GR|G
// G|BG|B
copyMakeBorder(BayerImage, BayerImage, 0, 0, 1, 1, cv::BORDER_REFLECT_101);
break;
case 3: //BGGR
// R|GR|G
// ------
// G|BG|B
// R|GR|G
// ------
// G|BG|B
copyMakeBorder(BayerImage, BayerImage, 1, 1, 1, 1, cv::BORDER_REFLECT_101);
break;
case 4: //GBRG
// RG
// --
// GB
// RG
// --
// GB
copyMakeBorder(BayerImage, BayerImage, 1, 1, 0, 0, cv::BORDER_REFLECT_101);
break;
default:
std::cerr << "Wrong Bayer Pattern Flag" << endl;
return;
}
switch(DemosaicingMethod){
case 1:
demosaic_smooth_hue(BayerImage, Dst);
break;
case 2:
demosaic_laplacian_corrected(BayerImage, Dst);
break;
case 3:
demosaic_GBTF(BayerImage, Dst);
break;
case 4:
demosaic_residual(BayerImage, Dst);
break;
default:
std::cerr << "Wrong Demosaicing Method Index" << endl;
return;
}
//Rect (x, y, width, height);
switch(BayerPatternFlag){ //turn all back from RGGB to Dst
case 1: //RGGB
break;
case 2: //GRBG
// R|GR|G
// G|BG|B
Dst = Dst(Rect(1, 0, BayerImage.cols - 2, BayerImage.rows));
break;
case 3: //BGGR
// R|GR|G
// ------
// G|BG|B
// R|GR|G
// ------
// G|BG|B
Dst = Dst(Rect(1, 1, BayerImage.cols - 2, BayerImage.rows - 2));
break;
case 4: //GBRG
// RG
// --
// GB
// RG
// --
// GB
Dst = Dst(Rect(0, 1, BayerImage.cols, BayerImage.rows - 2));
break;
}
return;
}