Reading this result: The Edge (Laplacian) kernel sums to 0, so flat regions cancel to gray and only intensity CHANGES survive — this is why it highlights edges. (Shown with a +128 gray bias so negative responses are visible.)
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How it works
Convolution replaces each pixel with a weighted sum of its neighborhood, where the weights are the kernel. Blur kernels are positive weights that sum to 1, so they average away noise while preserving brightness; edge kernels (Laplacian, Sobel) sum to 0, so flat regions cancel and only intensity changes remain. It is the core primitive of image processing and the convolutional layers in modern neural networks — try each kernel on the same test image to see exactly what its weights do.
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