今回はC++でNon-local Means Filterを自作して画像のノイズを除去する方法について記載する。
画像のノイズ除去を実施するときにNon-local Means Filterを使いたいことがある。
PythonのOpenCVでは「cv2.fastNlMeansDenoisingColored（）」を使うことができるが、C++版のOpenCV1.0ではこれに該当する関数がない。
よって、このような場合は「Non-local Means Filter」を自作する必要がある。
今回は以下のサイトにその内容があったので備忘録として残しておく。
Non-local Means Filterによるデノイジング
結果としてVisualStudioを起動して以下の内容を実装するとC++でNon-local Means Filterを実行することができた。
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#define _CRT_SECURE_NO_WARNINGS #define _USE_MATH_DEFINES #include <iostream> #include <cmath> #include <opencv2/opencv.hpp> using namespace std; using namespace cv; string win_src = "src"; string win_dst = "dst"; string win_msk = "msk"; // 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); cvtColor(src, ssrc, COLOR_YUV2BGR_NV12); cvtColor(dest, ddest, COLOR_YUV2BGR_NV12); } 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() { string file_src = "src.jpg"; string file_dst = "dst.jpg"; Mat img_src = imread(file_src, 1); Mat img_dst; if (!img_src.data) { cout << "error" << endl; return -1; } namedWindow(win_src, WINDOW_AUTOSIZE); namedWindow(win_dst, WINDOW_AUTOSIZE); //Non-local Means Filterによるノイズ除去 const double noise_sigma = 15.0; nonlocalMeansFilter(img_src, img_dst, 3, 7, noise_sigma, noise_sigma); imshow(win_src, img_src); imshow(win_dst, img_dst); imwrite(file_dst, img_dst); waitKey(0); return 0; } |
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