最新版OpenCV2.4.7中，cv::resize函数有五种插值算法：最近邻、双线性、双三次、基于像素区域关系、兰索斯插值。下面用for循环代替cv::resize函数来说明其详细的插值实现过程，其中部分代码摘自于cv::resize函数中的源代码。
每种插值算法的前部分代码是相同的，如下：
cv::Mat matSrc, matDst1, matDst2; matSrc = cv::imread("lena.jpg", 2 | 4); matDst1 = cv::Mat(cv::Size(800, 1000), matSrc.type(), cv::Scalar::all(0)); matDst2 = cv::Mat(matDst1.size(), matSrc.type(), cv::Scalar::all(0)); double scale_x = (double)matSrc.cols / matDst1.cols; double scale_y = (double)matSrc.rows / matDst1.rows;1、最近邻：公式，
for (int i = 0; i < matDst1.cols; ++i) { int sx = cvFloor(i * scale_x); sx = std::min(sx, matSrc.cols - 1); for (int j = 0; j < matDst1.rows; ++j) { int sy = cvFloor(j * scale_y); sy = std::min(sy, matSrc.rows - 1); matDst1.at
(j, i) = matSrc.at
(sy, sx); } } cv::imwrite("nearest_1.jpg", matDst1); cv::resize(matSrc, matDst2, matDst1.size(), 0, 0, 0); cv::imwrite("nearest_2.jpg", matDst2);
2、双线性：由相邻的四像素(2*2)计算得出，公式，
uchar* dataDst = matDst1.data; int stepDst = matDst1.step; uchar* dataSrc = matSrc.data; int stepSrc = matSrc.step; int iWidthSrc = matSrc.cols; int iHiehgtSrc = matSrc.rows; for (int j = 0; j < matDst1.rows; ++j) { float fy = (float)((j + 0.5) * scale_y - 0.5); int sy = cvFloor(fy); fy -= sy; sy = std::min(sy, iHiehgtSrc - 2); sy = std::max(0, sy); short cbufy[2]; cbufy[0] = cv::saturate_cast
((1.f - fy) * 2048); cbufy[1] = 2048 - cbufy[0]; for (int i = 0; i < matDst1.cols; ++i) { float fx = (float)((i + 0.5) * scale_x - 0.5); int sx = cvFloor(fx); fx -= sx; if (sx < 0) { fx = 0, sx = 0; } if (sx >= iWidthSrc - 1) { fx = 0, sx = iWidthSrc - 2; } short cbufx[2]; cbufx[0] = cv::saturate_cast
((1.f - fx) * 2048); cbufx[1] = 2048 - cbufx[0]; for (int k = 0; k < matSrc.channels(); ++k) { *(dataDst+ j*stepDst + 3*i + k) = (*(dataSrc + sy*stepSrc + 3*sx + k) * cbufx[0] * cbufy[0] + *(dataSrc + (sy+1)*stepSrc + 3*sx + k) * cbufx[0] * cbufy[1] + *(dataSrc + sy*stepSrc + 3*(sx+1) + k) * cbufx[1] * cbufy[0] + *(dataSrc + (sy+1)*stepSrc + 3*(sx+1) + k) * cbufx[1] * cbufy[1]) >> 22; } } } cv::imwrite("linear_1.jpg", matDst1); cv::resize(matSrc, matDst2, matDst1.size(), 0, 0, 1); cv::imwrite("linear_2.jpg", matDst2);
3、双三次：由相邻的4*4像素计算得出，公式类似于双线性
int iscale_x = cv::saturate_cast
(scale_x); int iscale_y = cv::saturate_cast
(scale_y); for (int j = 0; j < matDst1.rows; ++j) { float fy = (float)((j + 0.5) * scale_y - 0.5); int sy = cvFloor(fy); fy -= sy; sy = std::min(sy, matSrc.rows - 3); sy = std::max(1, sy); const float A = -0.75f; float coeffsY[4]; coeffsY[0] = ((A*(fy + 1) - 5*A)*(fy + 1) + 8*A)*(fy + 1) - 4*A; coeffsY[1] = ((A + 2)*fy - (A + 3))*fy*fy + 1; coeffsY[2] = ((A + 2)*(1 - fy) - (A + 3))*(1 - fy)*(1 - fy) + 1; coeffsY[3] = 1.f - coeffsY[0] - coeffsY[1] - coeffsY[2]; short cbufY[4]; cbufY[0] = cv::saturate_cast
(coeffsY[0] * 2048); cbufY[1] = cv::saturate_cast
(coeffsY[1] * 2048); cbufY[2] = cv::saturate_cast
(coeffsY[2] * 2048); cbufY[3] = cv::saturate_cast
(coeffsY[3] * 2048); for (int i = 0; i < matDst1.cols; ++i) { float fx = (float)((i + 0.5) * scale_x - 0.5); int sx = cvFloor(fx); fx -= sx; if (sx < 1) { fx = 0, sx = 1; } if (sx >= matSrc.cols - 3) { fx = 0, sx = matSrc.cols - 3; } float coeffsX[4]; coeffsX[0] = ((A*(fx + 1) - 5*A)*(fx + 1) + 8*A)*(fx + 1) - 4*A; coeffsX[1] = ((A + 2)*fx - (A + 3))*fx*fx + 1; coeffsX[2] = ((A + 2)*(1 - fx) - (A + 3))*(1 - fx)*(1 - fx) + 1; coeffsX[3] = 1.f - coeffsX[0] - coeffsX[1] - coeffsX[2]; short cbufX[4]; cbufX[0] = cv::saturate_cast
(coeffsX[0] * 2048); cbufX[1] = cv::saturate_cast
(coeffsX[1] * 2048); cbufX[2] = cv::saturate_cast
(coeffsX[2] * 2048); cbufX[3] = cv::saturate_cast
(coeffsX[3] * 2048); for (int k = 0; k < matSrc.channels(); ++k) { matDst1.at
(j, i)[k] = abs((matSrc.at
(sy-1, sx-1)[k] * cbufX[0] * cbufY[0] + matSrc.at
(sy, sx-1)[k] * cbufX[0] * cbufY[1] + matSrc.at
(sy+1, sx-1)[k] * cbufX[0] * cbufY[2] + matSrc.at
(sy+2, sx-1)[k] * cbufX[0] * cbufY[3] + matSrc.at
(sy-1, sx)[k] * cbufX[1] * cbufY[0] + matSrc.at
(sy, sx)[k] * cbufX[1] * cbufY[1] + matSrc.at
(sy+1, sx)[k] * cbufX[1] * cbufY[2] + matSrc.at
(sy+2, sx)[k] * cbufX[1] * cbufY[3] + matSrc.at
(sy-1, sx+1)[k] * cbufX[2] * cbufY[0] + matSrc.at
(sy, sx+1)[k] * cbufX[2] * cbufY[1] + matSrc.at
(sy+1, sx+1)[k] * cbufX[2] * cbufY[2] + matSrc.at
(sy+2, sx+1)[k] * cbufX[2] * cbufY[3] + matSrc.at
(sy-1, sx+2)[k] * cbufX[3] * cbufY[0] + matSrc.at
(sy, sx+2)[k] * cbufX[3] * cbufY[1] + matSrc.at
(sy+1, sx+2)[k] * cbufX[3] * cbufY[2] + matSrc.at
(sy+2, sx+2)[k] * cbufX[3] * cbufY[3] ) >> 22); } } } cv::imwrite("cubic_1.jpg", matDst1); cv::resize(matSrc, matDst2, matDst1.size(), 0, 0, 2); cv::imwrite("cubic_2.jpg", matDst2);
4、基于像素区域关系：共分三种情况，图像放大时类似于双线性插值，图像缩小(x轴、y轴同时缩小)又分两种情况，此情况下可以避免波纹出现。
#ifdef _MSC_VER cv::resize(matSrc, matDst2, matDst1.size(), 0, 0, 3); cv::imwrite("E:/GitCode/OpenCV_Test/test_images/area_2.jpg", matDst2); #else cv::resize(matSrc, matDst2, matDst1.size(), 0, 0, 3); cv::imwrite("area_2.jpg", matDst2); #endif fprintf(stdout, "==== start area ====\n"); double inv_scale_x = 1. / scale_x; double inv_scale_y = 1. / scale_y; int iscale_x = cv::saturate_cast
(scale_x); int iscale_y = cv::saturate_cast
(scale_y); bool is_area_fast = std::abs(scale_x - iscale_x) < DBL_EPSILON && std::abs(scale_y - iscale_y) < DBL_EPSILON; if (scale_x >= 1 && scale_y >= 1) { // zoom out if (is_area_fast) { // integer multiples for (int j = 0; j < matDst1.rows; ++j) { int sy = std::min(cvFloor(j * scale_y), matSrc.rows - 1); for (int i = 0; i < matDst1.cols; ++i) { int sx = std::min(cvFloor(i * scale_x), matSrc.cols -1); matDst1.at
(j, i) = matSrc.at
(sy, sx); } } #ifdef _MSC_VER cv::imwrite("E:/GitCode/OpenCV_Test/test_images/area_1.jpg", matDst1); #else cv::imwrite("area_1.jpg", matDst1); #endif return 0; } for (int j = 0; j < matDst1.rows; ++j) { double fsy1 = j * scale_y; double fsy2 = fsy1 + scale_y; double cellHeight = cv::min(scale_y, matSrc.rows - fsy1); int sy1 = cvCeil(fsy1), sy2 = cvFloor(fsy2); sy2 = std::min(sy2, matSrc.rows - 2); sy1 = std::min(sy1, sy2); float cbufy[2]; cbufy[0] = (float)((sy1 - fsy1) / cellHeight); cbufy[1] = (float)(std::min(std::min(fsy2 - sy2, 1.), cellHeight) / cellHeight); for (int i = 0; i < matDst1.cols; ++i) { double fsx1 = i * scale_x; double fsx2 = fsx1 + scale_x; double cellWidth = std::min(scale_x, matSrc.cols - fsx1); int sx1 = cvCeil(fsx1), sx2 = cvFloor(fsx2); sx2 = std::min(sx2, matSrc.cols - 2); sx1 = std::min(sx1, sx2); float cbufx[2]; cbufx[0] = (float)((sx1 - fsx1) / cellWidth); cbufx[1] = (float)(std::min(std::min(fsx2 - sx2, 1.), cellWidth) / cellWidth); for (int k = 0; k < matSrc.channels(); ++k) { matDst1.at
(j, i)[k] = (uchar)(matSrc.at
(sy1, sx1)[k] * cbufx[0] * cbufy[0] + matSrc.at
(sy1 + 1, sx1)[k] * cbufx[0] * cbufy[1] + matSrc.at
(sy1, sx1 + 1)[k] * cbufx[1] * cbufy[0] + matSrc.at
(sy1 + 1, sx1 + 1)[k] * cbufx[1] * cbufy[1]); } } } #ifdef _MSC_VER cv::imwrite("E:/GitCode/OpenCV_Test/test_images/area_1.jpg", matDst1); #else cv::imwrite("area_1.jpg", matDst1); #endif return 0; } //zoom in,it is emulated using some variant of bilinear interpolation for (int j = 0; j < matDst1.rows; ++j) { int sy = cvFloor(j * scale_y); float fy = (float)((j + 1) - (sy + 1) * inv_scale_y); fy = fy <= 0 ? 0.f : fy - cvFloor(fy); sy = std::min(sy, matSrc.rows - 2); short cbufy[2]; cbufy[0] = cv::saturate_cast
((1.f - fy) * 2048); cbufy[1] = 2048 - cbufy[0]; for (int i = 0; i < matDst1.cols; ++i) { int sx = cvFloor(i * scale_x); float fx = (float)((i + 1) - (sx + 1) * inv_scale_x); fx = fx < 0 ? 0.f : fx - cvFloor(fx); if (sx < 0) { fx = 0, sx = 0; } if (sx >= matSrc.cols - 1) { fx = 0, sx = matSrc.cols - 2; } short cbufx[2]; cbufx[0] = cv::saturate_cast
((1.f - fx) * 2048); cbufx[1] = 2048 - cbufx[0]; for (int k = 0; k < matSrc.channels(); ++k) { matDst1.at
(j, i)[k] = (matSrc.at
(sy, sx)[k] * cbufx[0] * cbufy[0] + matSrc.at
(sy + 1, sx)[k] * cbufx[0] * cbufy[1] + matSrc.at
(sy, sx + 1)[k] * cbufx[1] * cbufy[0] + matSrc.at
(sy + 1, sx + 1)[k] * cbufx[1] * cbufy[1]) >> 22; } } } fprintf(stdout, "==== end area ====\n"); #ifdef _MSC_VER cv::imwrite("E:/GitCode/OpenCV_Test/test_images/area_1.jpg", matDst1); #else cv::imwrite("area_1.jpg", matDst1); #endif
注：以上基于area进行图像缩小的代码有问题，具体实现代码可以参考https://github.com/fengbingchun/OpenCV_Test/blob/master/src/fbc_cv/include/resize.hpp，用法如下：
fbc::Mat3BGR src(matSrc.rows, matSrc.cols, matSrc.data); fbc::Mat3BGR dst(matDst1.rows, matDst1.cols, matDst1.data); fbc::resize(src, dst, 3);5、兰索斯插值：由相邻的8*8像素计算得出，公式类似于双线性
int iscale_x = cv::saturate_cast
(scale_x); int iscale_y = cv::saturate_cast
(scale_y); for (int j = 0; j < matDst1.rows; ++j) { float fy = (float)((j + 0.5) * scale_y - 0.5); int sy = cvFloor(fy); fy -= sy; sy = std::min(sy, matSrc.rows - 5); sy = std::max(3, sy); const double s45 = 0.; const double cs[][2] = {
{1, 0}, {-s45, -s45}, {0, 1}, {s45, -s45}, {-1, 0}, {s45, s45}, {0, -1}, {-s45, s45}}; float coeffsY[8]; if (fy < FLT_EPSILON) { for (int t = 0; t < 8; t++) coeffsY[t] = 0; coeffsY[3] = 1; } else { float sum = 0; double y0 = -(fy + 3) * CV_PI * 0.25, s0 = sin(y0), c0 = cos(y0); for (int t = 0; t < 8; ++t) { double dy = -(fy + 3 -t) * CV_PI * 0.25; coeffsY[t] = (float)((cs[t][0] * s0 + cs[t][1] * c0) / (dy * dy)); sum += coeffsY[t]; } sum = 1.f / sum; for (int t = 0; t < 8; ++t) coeffsY[t] *= sum; } short cbufY[8]; cbufY[0] = cv::saturate_cast
(coeffsY[0] * 2048); cbufY[1] = cv::saturate_cast
(coeffsY[1] * 2048); cbufY[2] = cv::saturate_cast
(coeffsY[2] * 2048); cbufY[3] = cv::saturate_cast
(coeffsY[3] * 2048); cbufY[4] = cv::saturate_cast
(coeffsY[4] * 2048); cbufY[5] = cv::saturate_cast
(coeffsY[5] * 2048); cbufY[6] = cv::saturate_cast
(coeffsY[6] * 2048); cbufY[7] = cv::saturate_cast
(coeffsY[7] * 2048); for (int i = 0; i < matDst1.cols; ++i) { float fx = (float)((i + 0.5) * scale_x - 0.5); int sx = cvFloor(fx); fx -= sx; if (sx < 3) { fx = 0, sx = 3; } if (sx >= matSrc.cols - 5) { fx = 0, sx = matSrc.cols - 5; } float coeffsX[8]; if (fx < FLT_EPSILON) { for ( int t = 0; t < 8; t++ ) coeffsX[t] = 0; coeffsX[3] = 1; } else { float sum = 0; double x0 = -(fx + 3) * CV_PI * 0.25, s0 = sin(x0), c0 = cos(x0); for (int t = 0; t < 8; ++t) { double dx = -(fx + 3 -t) * CV_PI * 0.25; coeffsX[t] = (float)((cs[t][0] * s0 + cs[t][1] * c0) / (dx * dx)); sum += coeffsX[t]; } sum = 1.f / sum; for (int t = 0; t < 8; ++t) coeffsX[t] *= sum; } short cbufX[8]; cbufX[0] = cv::saturate_cast
(coeffsX[0] * 2048); cbufX[1] = cv::saturate_cast
(coeffsX[1] * 2048); cbufX[2] = cv::saturate_cast
(coeffsX[2] * 2048); cbufX[3] = cv::saturate_cast
(coeffsX[3] * 2048); cbufX[4] = cv::saturate_cast
(coeffsX[4] * 2048); cbufX[5] = cv::saturate_cast
(coeffsX[5] * 2048); cbufX[6] = cv::saturate_cast
(coeffsX[6] * 2048); cbufX[7] = cv::saturate_cast
(coeffsX[7] * 2048); for (int k = 0; k < matSrc.channels(); ++k) { matDst1.at
(j, i)[k] = abs((matSrc.at
(sy-3, sx-3)[k] * cbufX[0] * cbufY[0] + matSrc.at
(sy-2, sx-3)[k] * cbufX[0] * cbufY[1] + matSrc.at
(sy-1, sx-3)[k] * cbufX[0] * cbufY[2] + matSrc.at
(sy, sx-3)[k] * cbufX[0] * cbufY[3] + matSrc.at
(sy+1, sx-3)[k] * cbufX[0] * cbufY[4] + matSrc.at
(sy+2, sx-3)[k] * cbufX[0] * cbufY[5] + matSrc.at
(sy+3, sx-3)[k] * cbufX[0] * cbufY[6] + matSrc.at
(sy+4, sx-3)[k] * cbufX[0] * cbufY[7] + matSrc.at
(sy-3, sx-2)[k] * cbufX[1] * cbufY[0] + matSrc.at
(sy-2, sx-2)[k] * cbufX[1] * cbufY[1] + matSrc.at
(sy-1, sx-2)[k] * cbufX[1] * cbufY[2] + matSrc.at
(sy, sx-2)[k] * cbufX[1] * cbufY[3] + matSrc.at
(sy+1, sx-2)[k] * cbufX[1] * cbufY[4] + matSrc.at
(sy+2, sx-2)[k] * cbufX[1] * cbufY[5] + matSrc.at
(sy+3, sx-2)[k] * cbufX[1] * cbufY[6] + matSrc.at
(sy+4, sx-2)[k] * cbufX[1] * cbufY[7] + matSrc.at
(sy-3, sx-1)[k] * cbufX[2] * cbufY[0] + matSrc.at
(sy-2, sx-1)[k] * cbufX[2] * cbufY[1] + matSrc.at
(sy-1, sx-1)[k] * cbufX[2] * cbufY[2] + matSrc.at
(sy, sx-1)[k] * cbufX[2] * cbufY[3] + matSrc.at
(sy+1, sx-1)[k] * cbufX[2] * cbufY[4] + matSrc.at
(sy+2, sx-1)[k] * cbufX[2] * cbufY[5] + matSrc.at
(sy+3, sx-1)[k] * cbufX[2] * cbufY[6] + matSrc.at
(sy+4, sx-1)[k] * cbufX[2] * cbufY[7] + matSrc.at
(sy-3, sx)[k] * cbufX[3] * cbufY[0] + matSrc.at
(sy-2, sx)[k] * cbufX[3] * cbufY[1] + matSrc.at
(sy-1, sx)[k] * cbufX[3] * cbufY[2] + matSrc.at
(sy, sx)[k] * cbufX[3] * cbufY[3] + matSrc.at
(sy+1, sx)[k] * cbufX[3] * cbufY[4] + matSrc.at
(sy+2, sx)[k] * cbufX[3] * cbufY[5] + matSrc.at
(sy+3, sx)[k] * cbufX[3] * cbufY[6] + matSrc.at
(sy+4, sx)[k] * cbufX[3] * cbufY[7] + matSrc.at
(sy-3, sx+1)[k] * cbufX[4] * cbufY[0] + matSrc.at
(sy-2, sx+1)[k] * cbufX[4] * cbufY[1] + matSrc.at
(sy-1, sx+1)[k] * cbufX[4] * cbufY[2] + matSrc.at
(sy, sx+1)[k] * cbufX[4] * cbufY[3] + matSrc.at
(sy+1, sx+1)[k] * cbufX[4] * cbufY[4] + matSrc.at
(sy+2, sx+1)[k] * cbufX[4] * cbufY[5] + matSrc.at
(sy+3, sx+1)[k] * cbufX[4] * cbufY[6] + matSrc.at
(sy+4, sx+1)[k] * cbufX[4] * cbufY[7] + matSrc.at
(sy-3, sx+2)[k] * cbufX[5] * cbufY[0] + matSrc.at
(sy-2, sx+2)[k] * cbufX[5] * cbufY[1] + matSrc.at
(sy-1, sx+2)[k] * cbufX[5] * cbufY[2] + matSrc.at
(sy, sx+2)[k] * cbufX[5] * cbufY[3] + matSrc.at
(sy+1, sx+2)[k] * cbufX[5] * cbufY[4] + matSrc.at
(sy+2, sx+2)[k] * cbufX[5] * cbufY[5] + matSrc.at
(sy+3, sx+2)[k] * cbufX[5] * cbufY[6] + matSrc.at
(sy+4, sx+2)[k] * cbufX[5] * cbufY[7] + matSrc.at
(sy-3, sx+3)[k] * cbufX[6] * cbufY[0] + matSrc.at
(sy-2, sx+3)[k] * cbufX[6] * cbufY[1] + matSrc.at
(sy-1, sx+3)[k] * cbufX[6] * cbufY[2] + matSrc.at
(sy, sx+3)[k] * cbufX[6] * cbufY[3] + matSrc.at
(sy+1, sx+3)[k] * cbufX[6] * cbufY[4] + matSrc.at
(sy+2, sx+3)[k] * cbufX[6] * cbufY[5] + matSrc.at
(sy+3, sx+3)[k] * cbufX[6] * cbufY[6] + matSrc.at
(sy+4, sx+3)[k] * cbufX[6] * cbufY[7] + matSrc.at
(sy-3, sx+4)[k] * cbufX[7] * cbufY[0] + matSrc.at
(sy-2, sx+4)[k] * cbufX[7] * cbufY[1] + matSrc.at
(sy-1, sx+4)[k] * cbufX[7] * cbufY[2] + matSrc.at
(sy, sx+4)[k] * cbufX[7] * cbufY[3] + matSrc.at
(sy+1, sx+4)[k] * cbufX[7] * cbufY[4] + matSrc.at
(sy+2, sx+4)[k] * cbufX[7] * cbufY[5] + matSrc.at
(sy+3, sx+4)[k] * cbufX[7] * cbufY[6] + matSrc.at
(sy+4, sx+4)[k] * cbufX[7] * cbufY[7] ) >> 22);// } } } cv::imwrite("Lanczos_1.jpg", matDst1); cv::resize(matSrc, matDst2, matDst1.size(), 0, 0, 4); cv::imwrite("Lanczos_2.jpg", matDst2);
以上代码的实现结果与cv::resize函数相同，但是执行效率非常低，只是为了详细说明插值过程。OpenCV中默认采用C++ Concurrency进行优化加速，你也可以采用TBB、OpenMP等进行优化加速。
GitHub：https://github.com/fengbingchun/OpenCV_Test/blob/master/demo/OpenCV_Test/test_opencv_funset.cpp
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