如何获得正确的alpha值，以完美地融合两个图像？ [英] How to obtain the right alpha value to perfectly blend two images?

问题描述

我一直在尝试混合两个图片。我现在的方法是，我获得的重叠区域的两个图像的坐标，并且只有重叠区域，我混合与0.5的硬编码的alpha，然后添加它。 SO基本上我只是从两个图像的重叠区域取每个像素的值的一半，并添加它们。这不会给我一个完美的混合，因为alpha值硬编码为0.5。这里是混合3个图像的结果：

正如你可以看到，从一个图像到另一个图像的过渡仍然可见。如何获得完美的alpha值，以消除这种可见的转变？

这里是我目前在做混合的方式：

$范围（3）中的b的$ b

 ：
 base_img_warp [overlap_coords [0]，overlap_coords [1]，i] = base_img_warp [overlap_coords [0]，overlap_coords [1]，i] * 0.5 
 next_img_warp [overlap_coords [0]，overlap_coords [1]，i] = next_img_warp [overlap_coords [0]，overlap_coords [1]，i] * 0.5 
 final_img = cv2 .add（base_img_warp，next_img_warp）
  

如果任何人想给它一个镜头，图片和重叠区域的掩码：

混合面具（就像曝光一样，必须是浮动矩阵）：

图片拼贴：

I've been trying to blend two images. The current approach I'm taking is, I obtain the coordinates of the overlapping region of the two images, and only for the overlapping regions, I blend with a hardcoded alpha of 0.5, before adding it. SO basically I'm just taking half the value of each pixel from overlapping regions of both the images, and adding them. That doesn't give me a perfect blend because the alpha value is hardcoded to 0.5. Here's the result of blending of 3 images:

As you can see, the transition from one image to another is still visible. How do I obtain the perfect alpha value that would eliminate this visible transition? Or is there no such thing, and I'm taking a wrong approach?

Here's how I'm currently doing the blending:

for i in range(3):
            base_img_warp[overlap_coords[0], overlap_coords[1], i] = base_img_warp[overlap_coords[0], overlap_coords[1],i]*0.5
            next_img_warp[overlap_coords[0], overlap_coords[1], i] = next_img_warp[overlap_coords[0], overlap_coords[1],i]*0.5
final_img = cv2.add(base_img_warp, next_img_warp)

If anyone would like to give it a shot, here are two warped images, and the mask of their overlapping region: http://imgur.com/a/9pOsQ

解决方案

Here is the way I would do it in general:

int main(int argc, char* argv[])
{
    cv::Mat input1 = cv::imread("C:/StackOverflow/Input/pano1.jpg");
    cv::Mat input2 = cv::imread("C:/StackOverflow/Input/pano2.jpg");

    // compute the vignetting masks. This is much easier before warping, but I will try...
    // it can be precomputed, if the size and position of your ROI in the image doesnt change and can be precomputed and aligned, if you can determine the ROI for every image
    // the compression artifacts make it a little bit worse here, I try to extract all the non-black regions in the images.
    cv::Mat mask1;
    cv::inRange(input1, cv::Vec3b(10, 10, 10), cv::Vec3b(255, 255, 255), mask1);
    cv::Mat mask2;
    cv::inRange(input2, cv::Vec3b(10, 10, 10), cv::Vec3b(255, 255, 255), mask2);


    // now compute the distance from the ROI border:
    cv::Mat dt1;
    cv::distanceTransform(mask1, dt1, CV_DIST_L1, 3);
    cv::Mat dt2;
    cv::distanceTransform(mask2, dt2, CV_DIST_L1, 3);

    // now you can use the distance values for blending directly. If the distance value is smaller this means that the value is worse (your vignetting becomes worse at the image border)
    cv::Mat mosaic = cv::Mat(input1.size(), input1.type(), cv::Scalar(0, 0, 0));
    for (int j = 0; j < mosaic.rows; ++j)
    for (int i = 0; i < mosaic.cols; ++i)
    {
        float a = dt1.at<float>(j, i);
        float b = dt2.at<float>(j, i);

        float alpha = a / (a + b); // distances are not between 0 and 1 but this value is. The "better" a is, compared to b, the higher is alpha.
        // actual blending: alpha*A + beta*B
        mosaic.at<cv::Vec3b>(j, i) = alpha*input1.at<cv::Vec3b>(j, i) + (1 - alpha)* input2.at<cv::Vec3b>(j, i);
    }

    cv::imshow("mosaic", mosaic);

    cv::waitKey(0);
    return 0;
}

Basically you compute the distance from your ROI border to the center of your objects and compute the alpha from both blending mask values. So if one image has a high distance from the border and other one a low distance from border, you prefer the pixel that is closer to the image center. It would be better to normalize those values for cases where the warped images aren't of similar size. But even better and more efficient is to precompute the blending masks and warp them. Best would be to know the vignetting of your optical system and choose and identical blending mask (typically lower values of the border).

From the previous code you'll get these results: ROI masks:

Blending masks (just as an impression, must be float matrices instead):

image mosaic:

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