Guided filter is an edge-preserving smoothing filter like the bilateral filter. It is straightforward to implement and has linear complexity independent of the kernel size. For more details about this filter see [Kaiming10].
The interface consists of one simple function guidedFilter and a class GuidedFilter. If you have multiple images to filter with the same guidance image then use GuidedFilter class to avoid extra computations on initialization stage. The code supports single-channel and 3-channel (color) guidance images and CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F and CV_64F data types.
These examples are adapted from the original MATLAB implementation.
cv::Mat I = cv::imread("./img_smoothing/cat.bmp", CV_LOAD_IMAGE_GRAYSCALE);
cv::Mat p = I;
int r = 4; // try r=2, 4, or 8
double eps = 0.2 * 0.2; // try eps=0.1^2, 0.2^2, 0.4^2
eps *= 255 * 255; // Because the intensity range of our images is [0, 255]
cv::Mat q = guidedFilter(I, p, r, eps);
cv::Mat I = cv::imread("./img_flash/cave-flash.bmp", CV_LOAD_IMAGE_COLOR);
cv::Mat p = cv::imread("./img_flash/cave-noflash.bmp", CV_LOAD_IMAGE_COLOR);
int r = 8;
double eps = 0.02 * 0.02;
eps *= 255 * 255; // Because the intensity range of our images is [0, 255]
cv::Mat q = guidedFilter(I, p, r, eps);
cv::Mat I = cv::imread("./img_feathering/toy.bmp", CV_LOAD_IMAGE_COLOR);
cv::Mat p = cv::imread("./img_feathering/toy-mask.bmp", CV_LOAD_IMAGE_GRAYSCALE);
int r = 60;
double eps = 1e-6;
eps *= 255 * 255; // Because the intensity range of our images is [0, 255]
cv::Mat q = guidedFilter(I, p, r, eps);
cv::Mat I = cv::imread("./img_enhancement/tulips.bmp", CV_LOAD_IMAGE_COLOR);
I.convertTo(I, CV_32F, 1.0 / 255.0);
cv::Mat p = I;
int r = 16;
double eps = 0.1 * 0.1;
cv::Mat q = guidedFilter(I, p, r, eps);
cv::Mat I_enhanced = (I - q) * 5 + q;
MIT license.