图像信息熵
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//单幅图像信息熵计算double Entropy(Mat img){ double temp[256] = { 0.0 }; // 计算每个像素的累积值 for (int m = 0; m<img.rows; m++) {// 有效访问行列的方式 const uchar* t = img.ptr<uchar>(m); for (int n = 0; n<img.cols; n++) { int i = t[n]; temp[i] = temp[i] + 1; } } // 计算每个像素的概率 for (int i = 0; i<256; i++) { temp[i] = temp[i] / (img.rows*img.cols); } double result = 0; // 计算图像信息熵 for (int i = 0; i<256; i++) { if (temp[i] == 0.0) result = result; else result = result - temp[i] * (log(temp[i]) / log(2.0)); } return result;}// 两幅图像联合信息熵计算double ComEntropy(Mat img1, Mat img2, double img1_entropy, double img2_entropy){ double temp[256][256] = { 0.0 }; // 计算联合图像像素的累积值 for (int m1 = 0, m2 = 0; m1 < img1.rows, m2 < img2.rows; m1++, m2++) { // 有效访问行列的方式 const uchar* t1 = img1.ptr<uchar>(m1); const uchar* t2 = img2.ptr<uchar>(m2); for (int n1 = 0, n2 = 0; n1 < img1.cols, n2 < img2.cols; n1++, n2++) { int i = t1[n1], j = t2[n2]; temp[i][j] = temp[i][j] + 1; } } // 计算每个联合像素的概率 for (int i = 0; i < 256; i++) { for (int j = 0; j < 256; j++) { temp[i][j] = temp[i][j] / (img1.rows*img1.cols); } } double result = 0.0; //计算图像联合信息熵 for (int i = 0; i < 256; i++) { for (int j = 0; j < 256; j++) { if (temp[i][j] == 0.0) result = result; else result = result - temp[i][j] * (log(temp[i][j]) / log(2.0)); } } //得到两幅图像的互信息熵 img1_entropy = Entropy(img1); img2_entropy = Entropy(img2); result = img1_entropy + img2_entropy - result; return result;}
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