What does a gradient operator measure?

A gradient operator approximates changes in image intensity across neighboring pixels. Larger changes appear as stronger responses in the visualization.

How are Sobel, Scharr, and Prewitt different?

They use different convolution weights to approximate horizontal and vertical derivatives. Comparing them can show how kernel weighting changes the response.

What does Laplacian show?

Laplacian is a second-derivative response that emphasizes locations where the intensity slope changes.

Sviluppatore Strumento

Operatori di gradiente

First-derivative gradient operators are the foundation of edge detection. Sobel is the workhorse, Scharr offers slightly better rotational symmetry, Prewitt is a simpler unweighted alternative, and Laplacian is a second-derivative operator highlighting zero-crossings. This card lets you compare all four with controllable kernel size, direction, and strength.

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Gradient Operators

Compare first-derivative Sobel, Scharr, and Prewitt operators with the second-derivative Laplacian operator. The output visualizes intensity changes selected by the operator and direction controls.

Available Controls

Operator: Sobel, Scharr, Prewitt, or Laplacian
Direction: combined magnitude, horizontal, or vertical
Kernel size: 1, 3, 5, or 7 for Sobel and Laplacian
Visualization strength: 0.5 to 4.0
Output format: PNG or JPG

How to Use Gradient Operators

Select an image — Choose the JPG, PNG, or WebP image to analyze.
Choose gradient settings — Select an operator and direction, then adjust supported kernel and strength controls.
Review and download — Inspect the gradient map, process it, and save the generated PNG or JPG.

Frequently Asked Questions

What does a gradient operator measure?: A gradient operator approximates changes in image intensity across neighboring pixels. Larger changes appear as stronger responses in the visualization.
How are Sobel, Scharr, and Prewitt different?: They use different convolution weights to approximate horizontal and vertical derivatives. Comparing them can show how kernel weighting changes the response.
What does Laplacian show?: Laplacian is a second-derivative response that emphasizes locations where the intensity slope changes.

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