中文名

双线性插值

外文名

Bilinear Interpolation

第一步：X方向的线性插值,插入蓝色 第二步 ：做完X方向的插值后再做Y方向的

x方向上

Y方向上插入绿色点P.

线性插值的结果与插值的顺序无关。首先进行y方向的插值，然后进行x方向的插值，所得到的结果是一样的。但双线性插值插值方法这种方法并不是线性的，首先进行y方向的插值，然后进行x方向的插值，与首先进行 x方向的插值，然后进行 y方向的插值，所得到的R1与R2是不一样的。

IplImage *img = cvLoadImage("C:\\Users\\Administrator\\Desktop\\3838.jpg");
int nw = img->width;
int nh = img->height;void Ctry::OnTryTyr1()
{//TODO:  在此添加命令处理程序代码  double times = 0.8;          //比例因子  int nWidth =int( times * (img->width));int nHeight =int( times * (img->height));IplImage *dst = cvCreateImage(cvSize(nWidth, nHeight), 8, 3);CvScalar pixel1;for (int i = 0; i < dst->width; i++){for (int j = 0; j < dst->height; j++){if (int(i * 1 / times) < img->width && int(j * 1 / times) < img->height){pixel1 = InterpBilinear((i * 1 / times), (j * 1 / times ));cvSet2D(dst, j, i, pixel1);}else{cvSet2D(dst, j, i, RGB(0, 0, 0, ));}}}cvSaveImage("C:\\Users\\Administrator\\Desktop\\InterpBilinear.jpg", dst);cvNamedWindow(" dst", CV_WINDOW_AUTOSIZE);cvShowImage(" dst", dst);cvNamedWindow("img", CV_WINDOW_AUTOSIZE);cvShowImage("img", img);cvWaitKey(0);cvReleaseImage(&img);cvReleaseImage(&dst);cvDestroyWindow(" dst");cvDestroyWindow("img");
}CvScalar Ctry::InterpBilinear(double x, double y)
{//4个邻近点的坐标（i1,j1）,（i2,j1）,（i1,j2）,（i2,j2）int x1, x2;int y1, y2;//4个最邻近像素值CvScalar f1, f2, f3, f4;//二个插值中间值CvScalar f12, f34;CvScalar pixel;double epsilon = 0.0001;//计算四个最邻近像素的坐标x1 = int(x);x2 = x1 + 1;y1 = int(y);y2 = y1 + 1;if ((x<0) || (x>nw - 1) || (y<0) || (y>nh - 1)){//要计算的点不在原图范围内MessageBox(NULL, "超过图片大小", "错误", MB_OK | MB_ICONERROR);return 0;}else{if (fabs(x - nw + 1 )<= epsilon){//要计算的点在图像的右边缘上if (fabs(y - nh + 1) <= epsilon){//要计算的元素恰好是图像最右下角的那一个元素，直接返回该点的像素值f1 = cvGet2D(img, y1, x1);return f1;}else{//在图像右边缘且不是最后一点，直接一次插值即可f1 = cvGet2D(img, y1, x1);f3 = cvGet2D(img, y1, x2);pixel.val[0] = f1.val[0] + (y - y1)*(f3.val[0] - f1.val[0]);pixel.val[1] = f1.val[1] + (y - y1)*(f3.val[1] - f1.val[1]);pixel.val[2] = f1.val[2] + (y - y1)*(f3.val[2] - f1.val[2]);return pixel;}}else if (fabs(y - nh + 1) <= epsilon){//要计算的点在图像的下边缘上且不是最后一点，直接一次插值即可f1 = cvGet2D(img, y1, x1);f2 = cvGet2D(img, y2, x1);pixel.val[0] = f1.val[0] + (x - x1)*(f2.val[0] - f1.val[0]);pixel.val[1] = f1.val[1] + (x - x1)*(f2.val[1] - f1.val[1]);pixel.val[2] = f1.val[2] + (x - x1)*(f2.val[2] - f1.val[2]);return pixel;}else{//计算4个最邻近像素值f1 = cvGet2D(img, y1, x1);f2 = cvGet2D(img, y2, x1);f3 = cvGet2D(img, y1, x2);f4 = cvGet2D(img, y2, x2);//插值1f12.val[0] = f1.val[0] + (x - x1)*(f2.val[0] - f1.val[0]);f12.val[1] = f1.val[1] + (x - x1)*(f2.val[1] - f1.val[1]);f12.val[2] = f1.val[2] + (x - x1)*(f2.val[2] - f1.val[2]);//插值2f34.val[0] = f3.val[0] + (x - x1)*(f4.val[0] - f3.val[0]);f34.val[1] = f3.val[1] + (x - x1)*(f4.val[1] - f3.val[1]);f34.val[2] = f3.val[2] + (x - x1)*(f4.val[2] - f3.val[2]);//插值3pixel.val[0] = f12.val[0] + (y - y1)*(f34.val[0] - f12.val[0]);pixel.val[1] = f12.val[1] + (y - y1)*(f34.val[1] - f12.val[1]);pixel.val[2] = f12.val[2] + (y - y1)*(f34.val[2] - f12.val[2]);return pixel;}}
}

效果图：