论文原文
https://wenku.baidu.com/view/f74cc087e53a580216fcfe52.html?from=search
算法介绍
论文的引言：对于光照不均匀的图像,用通常的图像分割方法不能取得满意的效果。为了解决这个问题,文中提出了一种实用 而简便的图像分割方法。该方法针对图像中不同亮度区域进行亮度补偿,使得整个图像亮度背景趋于一致后,再进行通常 的阈值分割。实验结果表明,用该方法能取得良好的分割效果。 关于常用的阈值分割不是我关注的，我这里只实现前面光照补偿的部分。算法的原理可以仔细看论文。
算法步骤
- 如果是RGB图需要转化成灰度图
- 求取原始图src的平均灰度，并记录rows和cols
- 按照一定大小，分为 DX×DY个方块，求出每块的平均值，得到子块的亮度矩阵D
- 用矩阵D的每个元素减去源图的平均灰度，得到子块的亮度差值矩阵E
- 用双立方插值法，将矩阵E resize成与源图一样大小的亮度分布矩阵R
- 得到矫正后的图像result=I - R
C++ 代码实现
Mat speed_rgb2gray(Mat src) {
Mat dst(src.rows, src.cols, CV_8UC1);
#pragma omp parallel for num_threads(12)
for (int i = 0; i < src.rows; i++) {
for (int j = 0; j < src.cols; j++) {
dst.at<uchar>(i, j) = ((src.at<Vec3b>(i, j)[0] << 18) + (src.at<Vec3b>(i, j)[0] << 15) + (src.at<Vec3b>(i, j)[0] << 14) +
(src.at<Vec3b>(i, j)[0] << 11) + (src.at<Vec3b>(i, j)[0] << 7) + (src.at<Vec3b>(i, j)[0] << 7) + (src.at<Vec3b>(i, j)[0] << 5) +
(src.at<Vec3b>(i, j)[0] << 4) + (src.at<Vec3b>(i, j)[0] << 2) +
(src.at<Vec3b>(i, j)[1] << 19) + (src.at<Vec3b>(i, j)[1] << 16) + (src.at<Vec3b>(i, j)[1] << 14) + (src.at<Vec3b>(i, j)[1] << 13) +
(src.at<Vec3b>(i, j)[1] << 10) + (src.at<Vec3b>(i, j)[1] << 8) + (src.at<Vec3b>(i, j)[1] << 4) + (src.at<Vec3b>(i, j)[1] << 3) + (src.at<Vec3b>(i, j)[1] << 1) +
(src.at<Vec3b>(i, j)[2] << 16) + (src.at<Vec3b>(i, j)[2] << 15) + (src.at<Vec3b>(i, j)[2] << 14) + (src.at<Vec3b>(i, j)[2] << 12) +
(src.at<Vec3b>(i, j)[2] << 9) + (src.at<Vec3b>(i, j)[2] << 7) + (src.at<Vec3b>(i, j)[2] << 6) + (src.at<Vec3b>(i, j)[2] << 5) + (src.at<Vec3b>(i, j)[2] << 4) + (src.at<Vec3b>(i, j)[2] << 1) >> 20);
}
}
return dst;
}
Mat unevenLightCompensate(Mat src, int block_Size) {
int row = src.rows;
int col = src.cols;
Mat gray(row, col, CV_8UC1);
if (src.channels() == 3) {
gray = speed_rgb2gray(src);
}
else {
gray = src;
}
float average = mean(gray)[0];
int new_row = ceil(1.0 * row / block_Size);
int new_col = ceil(1.0 * col / block_Size);
Mat new_img(new_row, new_col, CV_32FC1);
for (int i = 0; i < new_row; i++) {
for (int j = 0; j < new_col; j++) {
int rowx = i * block_Size;
int rowy = (i + 1) * block_Size;
int colx = j * block_Size;
int coly = (j + 1) * block_Size;
if (rowy > row) rowy = row;
if (coly > col) coly = col;
Mat ROI = src(Range(rowx, rowy), Range(colx, coly));
float block_average = mean(ROI)[0];
new_img.at<float>(i, j) = block_average;
}
}
new_img = new_img - average;
Mat new_img2;
resize(new_img, new_img2, Size(row, col), (0, 0), (0, 0), INTER_CUBIC);
Mat new_src;
gray.convertTo(new_src, CV_32FC1);
Mat dst = new_src - new_img2;
dst.convertTo(dst, CV_8UC1);
return dst;
}
京公网安备 11010502036488号