Contrast Limited Adaptive Histogram Equalization in 360 images

Question

I am currently applying the Contrast Limited Adaptive Histogram Equalization algorithm together with an algorithm to perform the photo denoise.

My problem is that I am working with 360 photos. As the contrast generates different values at the edges when I join the photo, the edge line is highly noticeable. How can I mitigate that line? What changes should I make so that it is not noticeable and the algorithm is applied consistently?

Original Photo:

Code to Contrast Limited Adaptive Histogram Equalization

    # 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

Result:

360 performed:

It is highly notorious line of separation because it is not taken into account that the photo is joined later. What can I do?

you could copy the border/join region to the input image, like adding the right part of the image to the left and adding the left part of the image to the right before performing CLAHE and crop the image afterwards. In theory: perform the joining before performing CLAHE — Micka, May 21 '21 at 10:29
It seems quite complex to me in terms of code. I'm new to image processing ... But looks like that could be a possible solution ... — João Santos, May 21 '21 at 10:34
I cant help you with python code, but it will be something like: `borderSize = N; create image with size width,height = original width + 2x bordersize, height = original height; copy subimage of the original image to subimages of the border-image; it's quite simple once you know how to address subimages in numpy — Micka, May 21 '21 at 10:53
cross-post: https://forum.opencv.org/t/contrast-limited-adaptive-histogram-equalization-in-360-images/3467 — Christoph Rackwitz, May 22 '21 at 14:52

score 6 · Accepted Answer · answered May 21 '21 at 12:27

Here's an answer for C++, you can probably convert it easily to python/numpy. The idea is to use a border region before performing CLAHE and crop the image afterwards.

These are the subimage regions in the original image:

and they will be copied the the left/right of the image like this:

Maybe you can reduce the size of the border strongly:

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);
}

Images: input as in your original post.

After creating the border:

After performing CLAHE (with border):

After cropping the CLAHE-border-image:

Contrast Limited Adaptive Histogram Equalization in 360 images

1 Answers1