[英]Contrast Limited Adaptive Histogram Equalization in 360 images
我目前正在应用对比度受限自适应直方图均衡算法和算法来执行照片去噪。
我的问题是我正在使用 360 度全景照片。 当我加入照片时,由于对比度在边缘产生不同的值,所以边缘线非常明显。 我怎样才能减轻那条线? 我应该进行哪些更改以使其不明显并且算法始终如一地应用?
原始照片:
对比有限自适应直方图均衡的代码
# CLAHE (Contrast Limited Adaptive Histogram Equalization)
clahe = cv2.createCLAHE(clipLimit=1., tileGridSize=(6, 6))
lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB) # convert from BGR to LAB color space
l, a, b = cv2.split(lab) # split on 3 different channels
l2 = clahe.apply(l) # apply CLAHE to the L-channel
lab = cv2.merge((l2, a, b)) # merge channels
img2 = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR) # convert from LAB to BGR
结果:
360 执行:
这是非常臭名昭著的分隔线,因为它没有考虑到照片是稍后加入的。 我能做些什么?
这是 C++ 的答案,您可以轻松地将其转换为 python/numpy。 这个想法是在执行 CLAHE 之前使用边界区域,然后裁剪图像。
也许您可以强烈减小边框的大小:
int main()
{
cv::Mat img = cv::imread("C:/data/SO_360.jpg");
int borderSize = img.cols / 4;
// make image that can have some border region
cv::Mat borderImage = cv::Mat(cv::Size(img.cols + 2 * borderSize, img.rows), img.type());
// posX, posY, width, height of the subimages
cv::Rect leftBorderRegion = cv::Rect(0, 0, borderSize, borderImage.rows);
cv::Rect rightBorderRegion = cv::Rect(borderImage.cols - borderSize, 0, borderSize, borderImage.rows);
cv::Rect imgRegion = cv::Rect(borderSize, 0, img.cols, borderImage.rows);
// original image regions to copy:
cv::Rect left = cv::Rect(0, 0, borderSize, borderImage.rows);
cv::Rect right = cv::Rect(img.cols - borderSize, 0, borderSize, img.rows);
cv::Rect full = cv::Rect(0, 0, img.cols, img.rows);
// perform copying to subimage (left part of the img goes to right part of the border image):
img(left).copyTo(borderImage(rightBorderRegion));
img(right).copyTo(borderImage(leftBorderRegion));
img.copyTo(borderImage(imgRegion));
cv::imwrite("SO_360_border.jpg", borderImage);
//# CLAHE(Contrast Limited Adaptive Histogram Equalization)
//clahe = cv2.createCLAHE(clipLimit = 1., tileGridSize = (6, 6))
// apply the CLAHE algorithm to the L channel
cv::Ptr<cv::CLAHE> clahe = cv::createCLAHE();
clahe->setClipLimit(1);
clahe->setTilesGridSize(cv::Size(6, 6));
cv::Mat lab;
cv::cvtColor(borderImage, lab, cv::COLOR_BGR2Lab); // # convert from BGR to LAB color space
std::vector<cv::Mat> labChannels; //l, a, b = cv2.split(lab) # split on 3 different channels
cv::split(lab, labChannels);
//l2 = clahe.apply(l) # apply CLAHE to the L - channel
cv::Mat dst;
clahe->apply(labChannels[0], dst);
labChannels[0] = dst;
//lab = cv2.merge((l2, a, b)) # merge channels
cv::merge(labChannels, lab);
//img2 = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR) # convert from LAB to BGR
cv::cvtColor(lab, dst, cv::COLOR_Lab2BGR);
cv::imwrite("SO_360_border_clahe.jpg", dst);
// to crop the image after performing clahe:
cv::Mat cropped = dst(imgRegion).clone();
cv::imwrite("SO_360_clahe.jpg", cropped);
}
图片:输入您的原始帖子。
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