python - How to obtain the right alpha value to perfectly blend two images?

Question

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

Answer 1

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: