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Non-local-Means-Filter.cpp
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Non-local-Means-Filter.cpp
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#include <opencv2/opencv.hpp>
#include <iostream>
using namespace cv;
using namespace std;
// additional functions/////////////////////////////////////
void addNoiseSoltPepperMono(Mat& src, Mat& dest,double per)
{
cv::RNG rng;
#pragma omp parallel for
for(int j=0;j<src.rows;j++)
{
uchar* s=src.ptr(j);
uchar* d=dest.ptr(j);
for(int i=0;i<src.cols;i++)
{
double a1 = rng.uniform((double)0, (double)1);
if(a1>per)
d[i]=s[i];
else
{
double a2 = rng.uniform((double)0, (double)1);
if(a2>0.5)d[i]=0;
else d[i]=255;
}
}
}
}
void addNoiseMono(Mat& src, Mat& dest,double sigma)
{
Mat s;
src.convertTo(s,CV_16S);
Mat n(s.size(),CV_16S);
randn(n,0,sigma);
Mat temp = s+n;
temp.convertTo(dest,CV_8U);
}
void addNoise(Mat&src, Mat& dest, double sigma,double sprate=0.0)
{
if(src.channels()==1)
{
addNoiseMono(src,dest,sigma);
if(sprate!=0)addNoiseSoltPepperMono(dest,dest,sprate);
return;
}
else
{
vector<Mat> s;
vector<Mat> d(src.channels());
split(src,s);
for(int i=0;i<src.channels();i++)
{
addNoiseMono(s[i],d[i],sigma);
if(sprate!=0)addNoiseSoltPepperMono(d[i],d[i],sprate);
}
cv::merge(d,dest);
}
}
static double getPSNR(Mat& src, Mat& dest)
{
int i,j;
double sse,mse,psnr;
sse = 0.0;
for(j=0;j<src.rows;j++)
{
uchar* d=dest.ptr(j);
uchar* s=src.ptr(j);
for(i=0;i<src.cols;i++)
{
sse += ((d[i] - s[i])*(d[i] - s[i]));
}
}
if(sse == 0.0)
{
return 0;
}
else
{
mse =sse /(double)(src.cols*src.rows);
psnr = 10.0*log10((255*255)/mse);
return psnr;
}
}
double calcPSNR(Mat& src, Mat& dest)
{
Mat ssrc;
Mat ddest;
if(src.channels()==1)
{
src.copyTo(ssrc);
dest.copyTo(ddest);
}
else
{
cvtColor(src,ssrc,CV_BGR2YUV);
cvtColor(dest,ddest,CV_BGR2YUV);
}
double sn = getPSNR(ssrc,ddest);
return sn;
}
////////////////////////////////////////////////////////////////////////////////
//main implementaion
void nonlocalMeansFilter(Mat& src, Mat& dest, int templeteWindowSize, int searchWindowSize, double h, double sigma=0.0)
{
if(templeteWindowSize>searchWindowSize)
{
cout<<"searchWindowSize should be larger than templeteWindowSize"<<endl;
return;
}
if(dest.empty())dest=Mat::zeros(src.size(),src.type());
const int tr = templeteWindowSize>>1;
const int sr = searchWindowSize>>1;
const int bb = sr+tr;
const int D = searchWindowSize*searchWindowSize;
const int H=D/2+1;
const double div = 1.0/(double)D;//search area div
const int tD = templeteWindowSize*templeteWindowSize;
const double tdiv = 1.0/(double)(tD);//templete square div
//create large size image for bounding box;
Mat im;
copyMakeBorder(src,im,bb,bb,bb,bb,cv::BORDER_DEFAULT);
//weight computation;
vector<double> weight(256*256*src.channels());
double* w = &weight[0];
const double gauss_sd = (sigma == 0.0) ? h :sigma;
double gauss_color_coeff = -(1.0/(double)(src.channels()))*(1.0/(h*h));
int emax;
for(int i = 0; i < 256*256*src.channels(); i++ )
{
double v = std::exp( max(i-2.0*gauss_sd*gauss_sd,0.0)*gauss_color_coeff);
w[i] = v;
if(v<0.001)
{
emax=i;
break;
}
}
for(int i = emax; i < 256*256*src.channels(); i++ )w[i] = 0.0;
if(src.channels()==3)
{
const int cstep = im.step-templeteWindowSize*3;
const int csstep = im.step-searchWindowSize*3;
#pragma omp parallel for
for(int j=0;j<src.rows;j++)
{
uchar* d = dest.ptr(j);
int* ww=new int[D];
double* nw=new double[D];
for(int i=0;i<src.cols;i++)
{
double tweight=0.0;
//search loop
uchar* tprt = im.data +im.step*(sr+j) + 3*(sr+i);
uchar* sptr2 = im.data +im.step*j + 3*i;
for(int l=searchWindowSize,count=D-1;l--;)
{
uchar* sptr = sptr2 +im.step*(l);
for (int k=searchWindowSize;k--;)
{
//templete loop
int e=0;
uchar* t = tprt;
uchar* s = sptr+3*k;
for(int n=templeteWindowSize;n--;)
{
for(int m=templeteWindowSize;m--;)
{
// computing color L2 norm
e += (s[0]-t[0])*(s[0]-t[0])+(s[1]-t[1])*(s[1]-t[1])+(s[2]-t[2])*(s[2]-t[2]);//L2 norm
s+=3,t+=3;
}
t+=cstep;
s+=cstep;
}
const int ediv = e*tdiv;
ww[count--]=ediv;
//get weighted Euclidean distance
tweight+=w[ediv];
}
}
//weight normalization
if(tweight==0.0)
{
for(int z=0;z<D;z++) nw[z]=0;
nw[H]=1;
}
else
{
double itweight=1.0/(double)tweight;
for(int z=0;z<D;z++) nw[z]=w[ww[z]]*itweight;
}
double r=0.0,g=0.0,b=0.0;
uchar* s = im.ptr(j+tr); s+=3*(tr+i);
for(int l=searchWindowSize,count=0;l--;)
{
for(int k=searchWindowSize;k--;)
{
r += s[0]*nw[count];
g += s[1]*nw[count];
b += s[2]*nw[count++];
s+=3;
}
s+=csstep;
}
d[0] = saturate_cast<uchar>(r);
d[1] = saturate_cast<uchar>(g);
d[2] = saturate_cast<uchar>(b);
d+=3;
}//i
delete[] ww;
delete[] nw;
}//j
}
else if(src.channels()==1)
{
const int cstep = im.step-templeteWindowSize;
const int csstep = im.step-searchWindowSize;
#pragma omp parallel for
for(int j=0;j<src.rows;j++)
{
uchar* d = dest.ptr(j);
int* ww=new int[D];
double* nw=new double[D];
for(int i=0;i<src.cols;i++)
{
double tweight=0.0;
//search loop
uchar* tprt = im.data +im.step*(sr+j) + (sr+i);
uchar* sptr2 = im.data +im.step*j + i;
for(int l=searchWindowSize,count=D-1;l--;)
{
uchar* sptr = sptr2 +im.step*(l);
for (int k=searchWindowSize;k--;)
{
//templete loop
int e=0;
uchar* t = tprt;
uchar* s = sptr+k;
for(int n=templeteWindowSize;n--;)
{
for(int m=templeteWindowSize;m--;)
{
// computing color L2 norm
e += (*s-*t)*(*s-*t);
s++,t++;
}
t+=cstep;
s+=cstep;
}
const int ediv = e*tdiv;
ww[count--]=ediv;
//get weighted Euclidean distance
tweight+=w[ediv];
}
}
//weight normalization
if(tweight==0.0)
{
for(int z=0;z<D;z++) nw[z]=0;
nw[H]=1;
}
else
{
double itweight=1.0/(double)tweight;
for(int z=0;z<D;z++) nw[z]=w[ww[z]]*itweight;
}
double v=0.0;
uchar* s = im.ptr(j+tr); s+=(tr+i);
for(int l=searchWindowSize,count=0;l--;)
{
for(int k=searchWindowSize;k--;)
{
v += *(s++)*nw[count++];
}
s+=csstep;
}
*(d++) = saturate_cast<uchar>(v);
}//i
delete[] ww;
delete[] nw;
}//j
}
}
int main(int argc, char** argv)
{
char* filename = argc >= 2 ? argv[1] : (char*)"gnfXj.jpg";
//(1) Reading image and add noise(standart deviation = 15)
const double noise_sigma = 15.0;
Mat src = imread(filename,1);
Mat snoise;
Mat dest;
addNoise(src,snoise,noise_sigma);
//(2) preview conventional method with PSNR
//(2-1) RAW
cout<<"RAW: "<<calcPSNR(src,snoise)<<endl<<endl;
imwrite("noise.png",snoise);
//(2-2) Gaussian Filter (7x7) sigma = 5
int64 pre = getTickCount();
GaussianBlur(snoise,dest,Size(7,7),5);
cout<<"time: "<<1000.0*(getTickCount()-pre)/(getTickFrequency())<<" ms"<<endl;
cout<<"gaussian: "<<calcPSNR(src,dest)<<endl<<endl;
imwrite("gaussian.png",dest);
//(2-3) median Filter (3x3)
pre = getTickCount();
medianBlur(snoise,dest,3);
cout<<"time: "<<1000.0*(getTickCount()-pre)/(getTickFrequency())<<" ms"<<endl;
cout<<"median: "<<calcPSNR(src,dest)<<endl<<endl;
imwrite("median.png",dest);
//(2-4) Bilateral Filter (7x7) color sigma = 35, space sigma = 5
pre = getTickCount();
bilateralFilter(snoise,dest,7,35,5);
cout<<"time: "<<1000.0*(getTickCount()-pre)/(getTickFrequency())<<" ms"<<endl;
cout<<"bilateral: "<<calcPSNR(src,dest)<<endl<<endl;
imwrite("bilateral.png",dest);
//(3) analizing of performance of Nonlocal means filter
pre = getTickCount();
nonlocalMeansFilter(snoise,dest,3,7,noise_sigma,noise_sigma);
cout<<"time: "<<1000.0*(getTickCount()-pre)/(getTickFrequency())<<" ms"<<endl;
cout<<"nonlocal: "<<calcPSNR(src,dest)<<endl<<endl;
imwrite("nonlocal.png",dest);
imshow("noise", snoise);
imshow("Non-local Means Filter", dest);
waitKey();
return 0;
}