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Thanks for your input. I am new in this field, so I am not sure if any existing blending algorithm would fit in this cas

Thank you for your comment. I tried doing that (explained by my 2nd paragraph). The output is the same as the original i

I tried doing that (explained by my 2nd paragraph). The output is the same as the original image

Pyramid Blending with Single input and Non-Vertical Boundary Hi All, Here is the input image. Say you do not have the

Pyramid Blending with Single input and Non-Vertical Boundary Hi All, Here is the input image. Say you do not have the

Laplacian output has many white dots (noise) Hi, I applied Gaussian, and then laplacian to images. The output has many

Hi,

I applied Gaussian, and then laplacian to images. The output has many white dots. Please check the following 2 outputs.

images[i].convertTo(img, CV_32F, 1.0f / 255.0f);
GaussianBlur(img, img, Size(5,5), 0, 0, BORDER_DEFAULT );
if (channels == 3) {
cvtColor(img, gray, COLOR_RGB2GRAY);
}
else {
img.copyTo(gray);
}
Laplacian(gray, contrast, CV_32F);

Could someone please explain why? And how to solve it? Thank you!

EDIT: The above code is shown to be valid by LBerger's post. My problem is due to the normalization of the 2 output images by their sum. After division, some pixels tend to infinity. Shown by my following code here

for (size_t i = 0; i < images.size(); i++) {    
    contrast_sum = constrast[i]
}
for (size_t i = 0; i < images.size(); i++) {
    contrast[i] /= contrast_sum;
}

Hi LBerger, Thank you so much for the help. I realized the problem is with the latter part of the code. I normalized the

Laplacian output has many white dots (noise) Hi, I applied Gaussian, and then laplacian to images. The output has many

by reading the source code, greyscale inputs work fine too... basically same behaviour as expected

1 channel images using Merge Mertens Hi, I want to implement exposure fusion on black/white images (1 channel 8 bits).