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OpenCV图像增强（二）——Retinex图像增强

时间：2020-12-01 01:28:28

前言

1.Retinex图像增强是一种高动态范围图像的新色调映射技术。而基础理论是，物体的颜色是由物体对长波（红色）、中波（绿色）、短波（蓝色）光线的反射能力来决定的，而不是由反射光强度的绝对值来决定的，物体的色彩不受光照非均匀性的影响，具有一致性，即retinex是以色感一致性（颜色恒常性）为基础的。不同于传统的线性、非线性的只能增强图像某一类特征的方法，Retinex可以在动态范围压缩、边缘增强和颜色恒常三个方面达到平衡，因此可以对各种不同类型的图像进行自适应的增强。

2.Retinex图像增强包括两个步骤，全局适应和人类视觉系统的局部自适应。在局部自适应过程中，这里使用引导滤波器代替原本的高斯滤波器以减少晕圈伪影。为了保证良好的再现和动态范围压缩，使用基于场景的亮度值的对比度增强因子。此外，引入自适应非线性偏移来处理对数函数的非线性强度。

3.导向滤波作为一种保边滤波，可以运用在很多场合，比如美颜，去雾。

代码实现

void ALTMRetinex(const Mat& src, Mat &dst, bool LocalAdaptation = false, bool ContrastCorrect = true){Mat temp, src_gray;src.convertTo(temp, CV_32FC3);//灰度图cvtColor(temp, src_gray, CV_BGR2GRAY);double LwMax;//得到最大值minMaxLoc(src_gray, NULL, &LwMax);Mat Lw_;const int num = src.rows * src.cols;//计算每个数组元素绝对值的自然对数cv::log(src_gray + 1e-3f, Lw_);//矩阵自然指数float LwAver = exp(cv::sum(Lw_)[0] / num);Mat Lg;log(src_gray / LwAver + 1.f, Lg);//矩阵除法cv::divide(Lg, log(LwMax / LwAver + 1.f), Lg);//局部自适应Mat Lout;if (LocalAdaptation){int kernelSize = floor(std::max(3, std::max(src.rows / 100, src.cols / 100)));Mat Lp, kernel = cv::getStructuringElement(MORPH_RECT, Size(kernelSize, kernelSize));cv::dilate(Lg, Lp, kernel);Mat Hg = guidedFilter(Lg, Lp, 10, 0.01f);double eta = 36;double LgMax;cv::minMaxLoc(Lg, NULL, &LgMax);Mat alpha = 1.0f + Lg * (eta / LgMax);Mat Lg_;cv::log(Lg + 1e-3f, Lg_);float LgAver = exp(cv::sum(Lg_)[0] / num);float lambda = 10;float beta = lambda * LgAver;cv::log(Lg / Hg + beta, Lout);cv::multiply(alpha, Lout, Lout);cv::normalize(Lout, Lout, 0, 255, NORM_MINMAX);}else{cv::normalize(Lg, Lout, 0, 255, NORM_MINMAX);}Mat gain(src.rows , src.cols, CV_32F);for (int i = 0; i < src.rows; i++){for (int j = 0; j < src.cols; j++){float x = src_gray.at<float>(i, j);float y = Lout.at<float>(i, j);if (0 == x) gain.at<float>(i, j) = y;else gain.at<float>(i, j) = y / x;}}Mat bgr[3];cv::split(temp, bgr);if (ContrastCorrect){// 校正图像对比度bgr[0] = (gain.mul(bgr[0] + src_gray) + bgr[0] - src_gray) *0.5f;bgr[1] = (gain.mul(bgr[1] + src_gray) + bgr[1] - src_gray) *0.5f;bgr[2] = (gain.mul(bgr[2] + src_gray) + bgr[2] - src_gray) *0.5f;}else{cv::multiply(bgr[0], gain, bgr[0]);cv::multiply(bgr[1], gain, bgr[1]);cv::multiply(bgr[2], gain, bgr[2]);}cv::merge(bgr, 3, dst);dst.convertTo(dst, CV_8UC3);}//导向滤波器Mat guidedFilter(cv::Mat& I, cv::Mat& p, int r, float eps){/*× GUIDEDFILTER O(N) 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_32FC1);I = _I;cv::Mat _p;p.convertTo(_p, CV_32FC1);p = _p;//因为opencv自带的boxFilter（）中的Size,比如9x9,我们说半径为4r = 2 * r + 1;//mean_I = boxfilter(I, r) ./ N;cv::Mat mean_I;cv::boxFilter(I, mean_I, CV_32FC1, cv::Size(r, r));//mean_p = boxfilter(p, r) ./ N;cv::Mat mean_p;cv::boxFilter(p, mean_p, CV_32FC1, cv::Size(r, r));//mean_Ip = boxfilter(I.*p, r) ./ N;cv::Mat mean_Ip;cv::boxFilter(I.mul(p), mean_Ip, CV_32FC1, 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_32FC1, 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_32FC1, cv::Size(r, r));//mean_b = boxfilter(b, r) ./ N;cv::Mat mean_b;cv::boxFilter(b, mean_b, CV_32FC1, cv::Size(r, r));//q = mean_a .* I + mean_b; % Eqn. (8) in the paper;cv::Mat q = mean_a.mul(I) + mean_b;return q;}

运行结果