用 OpenCV 实现 Guided Filter



最近发现 OpenCV 的 C++ 接口写得和 Matlab 实在是太相似了。我用 OpenCV 2.49 来实现 Guided Filter 的时候几乎是直接照抄作者公开的 Matlab 代码。废话不多直接贴我的代码：

cv::Mat guidedFilter(cv::Mat I, cv::Mat p, int r, double eps)
{
	/*
	% GUIDEDFILTER   O(1) time implementation of guided filter.
	%
	%   - guidance image: I (should be a gray-scale/single channel image)
	%   - filtering input image: p (should be a gray-scale/single channel image)
	%   - local window radius: r
	%   - regularization parameter: eps
	*/

	cv::Mat _I;
	I.convertTo(_I, CV_64FC1);
	I = _I;

	
	cv::Mat _p;
	p.convertTo(_p, CV_64FC1);
	p = _p;

	//[hei, wid] = size(I);
	int hei = I.rows;
	int wid = I.cols;

	//N = boxfilter(ones(hei, wid), r); % the size of each local patch; N=(2r+1)^2 except for boundary pixels.
	cv::Mat N;
	cv::boxFilter(cv::Mat::ones(hei, wid, I.type()), N, CV_64FC1, cv::Size(r, r));

	//mean_I = boxfilter(I, r) ./ N;
	cv::Mat mean_I;
	cv::boxFilter(I, mean_I, CV_64FC1, cv::Size(r, r));

	//mean_p = boxfilter(p, r) ./ N;
	cv::Mat mean_p;
	cv::boxFilter(p, mean_p, CV_64FC1, cv::Size(r, r));

	//mean_Ip = boxfilter(I.*p, r) ./ N;
	cv::Mat mean_Ip;
	cv::boxFilter(I.mul(p), mean_Ip, CV_64FC1, cv::Size(r, r));

	//cov_Ip = mean_Ip - mean_I .* mean_p; % this is the covariance of (I, p) in each local patch.
	cv::Mat cov_Ip = mean_Ip - mean_I.mul(mean_p);

	//mean_II = boxfilter(I.*I, r) ./ N;
	cv::Mat mean_II;
	cv::boxFilter(I.mul(I), mean_II, CV_64FC1, cv::Size(r, r));

	//var_I = mean_II - mean_I .* mean_I;
	cv::Mat var_I = mean_II - mean_I.mul(mean_I);

	//a = cov_Ip ./ (var_I + eps); % Eqn. (5) in the paper;	
	cv::Mat a = cov_Ip / (var_I + eps);

	//b = mean_p - a .* mean_I; % Eqn. (6) in the paper;
	cv::Mat b = mean_p - a.mul(mean_I);

	//mean_a = boxfilter(a, r) ./ N;
	cv::Mat mean_a;
	cv::boxFilter(a, mean_a, CV_64FC1, cv::Size(r, r));
	mean_a = mean_a / N;

	//mean_b = boxfilter(b, r) ./ N;
	cv::Mat mean_b;
	cv::boxFilter(b, mean_b, CV_64FC1, cv::Size(r, r));
	mean_b = mean_b / N;

	//q = mean_a .* I + mean_b; % Eqn. (8) in the paper;
	cv::Mat q = mean_a.mul(I) + mean_b;

	return q;
}

上面这段代码的注释就是 Guided Filter 的原作者给出的 Matlab 代码，我按照这些 Matlab 代码，一行一行改写就很快地完成了基于 OpenCV 的 Guided Filter 实现。我大致搜索了一下，似乎很难找到公开的 Guided Filter 实现，所以这里我分享一下我的代码。另，再次感谢 Guided Filter 的作者 Kaiming He 公开的 Matlab 源码。

http://research.microsoft.com/en-us/um/people/kahe/eccv10/