The Laplacian edge detector uses only one kernel. It calculates second-order derivatives in a single pass and detects zero crossings. In general, the second-order derivative is extremely sensitive to noise.
The kernel for the Laplacian edge detector is shown in the following screenshot:
The following is an example of gradient-based edge detection and Laplacian-based edge detection. We can see that the first-order derivative is calculated using gradient-based edge detection, and second-order derivatives are calculated using Laplacian edge detection:
In the next section, we will learn about an important concept called Canny edge detection.