专栏 | OpenCV图像处理专栏十二 |《基于二维伽马函数的光照不均匀图像自适应校正算法》
本文来自 @BBuf 的社区专栏 GiantPandaCV,文末扫码即可订阅专栏。
前言
这是OpenCV图像处理专栏的第十二篇文章,今天为大家介绍一个用于解决光照不均匀的图像自适应校正算法。光照不均匀其实是非常常见的一种状况,为了提升人类的视觉感受或者是为了提升诸如深度学习之类的算法准确性,人们在解决光照不均衡方面已经有大量的工作。一起来看看这篇论文使用的算法吧,论文名为:《基于二维伽马函数的光照不均匀图像自适应校正算法》。
算法原理
论文使用了Retinex的多尺度高斯滤波求取「光照分量」,然后使用了二「维Gamma函数」针对原图的「HSV空间的V(亮度)分量」进行亮度改变,得到结果。原理还是蛮简单的,因为是中文论文,且作者介绍得很清楚,我就不细说了,可以自己看论文,论文地址见附录。本文的重点在于对算法步骤的解读和OpenCV复现。
算法步骤
需要注意的点
文中公式5(二维Gamma变换) 有误,公式5为:
OpenCV C++代码复现
Mat RGB2HSV(Mat src) {
int row = src.rows;
int col = src.cols;
Mat dst(row, col, CV_32FC3);
for (int i = 0; i < row; i++) {
for (int j = 0; j < col; j++) {
float b = src.at<Vec3b>(i, j)[0] / 255.0;
float g = src.at<Vec3b>(i, j)[1] / 255.0;
float r = src.at<Vec3b>(i, j)[2] / 255.0;
float minn = min(r, min(g, b));
float maxx = max(r, max(g, b));
dst.at<Vec3f>(i, j)[2] = maxx; //V
float delta = maxx - minn;
float h, s;
if (maxx != 0) {
s = delta / maxx;
}
else {
s = 0;
}
if (r == maxx) {
h = (g - b) / delta;
}
else if (g == maxx) {
h = 2 + (b - r) / delta;
}
else {
h = 4 + (r - g) / delta;
}
h *= 60;
if (h < 0)
h += 360;
dst.at<Vec3f>(i, j)[0] = h;
dst.at<Vec3f>(i, j)[1] = s;
}
}
return dst;
}
Mat HSV2RGB(Mat src) {
int row = src.rows;
int col = src.cols;
Mat dst(row, col, CV_8UC3);
float r, g, b, h, s, v;
for (int i = 0; i < row; i++) {
for (int j = 0; j < col; j++) {
h = src.at<Vec3f>(i, j)[0];
s = src.at<Vec3f>(i, j)[1];
v = src.at<Vec3f>(i, j)[2];
if (s == 0) {
r = g = b = v;
}
else {
h /= 60;
int offset = floor(h);
float f = h - offset;
float p = v * (1 - s);
float q = v * (1 - s * f);
float t = v * (1 - s * (1 - f));
switch (offset)
{
case 0: r = v; g = t; b = p; break;
case 1: r = q; g = v; b = p; break;
case 2: r = p; g = v; b = t; break;
case 3: r = p; g = q; b = v; break;
case 4: r = t; g = p; b = v; break;
case 5: r = v; g = p; b = q; break;
default:
break;
}
}
dst.at<Vec3b>(i, j)[0] = int(b * 255);
dst.at<Vec3b>(i, j)[1] = int(g * 255);
dst.at<Vec3b>(i, j)[2] = int(r * 255);
}
}
return dst;
}
Mat work(Mat src) {
int row = src.rows;
int col = src.cols;
Mat now = RGB2HSV(src);
Mat H(row, col, CV_32FC1);
Mat S(row, col, CV_32FC1);
Mat V(row, col, CV_32FC1);
for (int i = 0; i < row; i++) {
for (int j = 0; j < col; j++) {
H.at<float>(i, j) = now.at<Vec3f>(i, j)[0];
S.at<float>(i, j) = now.at<Vec3f>(i, j)[1];
V.at<float>(i, j) = now.at<Vec3f>(i, j)[2];
}
}
int kernel_size = min(row, col);
if (kernel_size % 2 == 0) {
kernel_size -= 1;
}
float SIGMA1 = 15;
float SIGMA2 = 80;
float SIGMA3 = 250;
float q = sqrt(2.0);
Mat F(row, col, CV_32FC1);
Mat F1, F2, F3;
GaussianBlur(V, F1, Size(kernel_size, kernel_size), SIGMA1 / q);
GaussianBlur(V, F2, Size(kernel_size, kernel_size), SIGMA2 / q);
GaussianBlur(V, F3, Size(kernel_size, kernel_size), SIGMA3 / q);
for (int i = 0; i < row; i++) {
for (int j = 0; j < col; j++) {
F.at <float>(i, j) = (F1.at<float>(i, j) + F2.at<float>(i, j) + F3.at<float>(i, j)) / 3.0;
}
}
float average = mean(F)[0];
Mat out(row, col, CV_32FC1);
for (int i = 0; i < row; i++) {
for (int j = 0; j < col; j++) {
float gamma = powf(0.5, (average - F.at<float>(i, j)) / average);
out.at<float>(i, j) = powf(V.at<float>(i, j), gamma);
}
}
vector <Mat> v;
v.push_back(H);
v.push_back(S);
v.push_back(out);
Mat merge_;
merge(v, merge_);
Mat dst = HSV2RGB(merge_);
return dst;
}
效果
结论
可以看到这个算法对光照不均匀的图像校正效果还是不错的,且没有像Retiex那样在亮度突变处出现色晕现象。
附录
论文原文:https://wenku.baidu.com/view/3570f2c255270722182ef74e.html
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版权声明:以上内容为用户推荐收藏至CareerEngine平台,其内容(含文字、图片、视频、音频等)及知识版权均属用户或用户转发自的第三方网站,如涉嫌侵权,请通知[email protected]进行信息删除。如需查看信息来源,请点击“查看原文”。如需洽谈其它事宜,请联系[email protected]。