What is an edge detection kernel?

It is a mathematical matrix (convolution kernel) applied to an image to calculate changes in pixel intensity, which helps identify sharp transitions or edges.

Which kernel should I choose?

Sobel is ideal for directional edges, Laplacian is excellent for detecting all edges simultaneously, and Roberts/Prewitt are useful for simpler, high-contrast boundary detection.

Can I invert the results?

Yes, enabling the 'Invert Edges' option will display black edges on a white background, which is often easier to analyze or print.

What image formats are supported?

You can upload JPEG, PNG, WebP, GIF, BMP, and TIFF files.

Does this tool change the image resolution?

No, the tool processes the pixel data using convolution filters but maintains the original dimensions of your uploaded image.

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Image Edge Detection Kernel

Apply edge detection convolution kernels to identify and highlight edges and contours in images

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6 options

FilesUpload source files for this workflow.1

Image FilefileRequired

Single file max size: 10 MBSupported types: image/jpeg, image/png, image/webp, image/gif, image/bmp, image/tiff

Image to apply edge detection kernel

SettingsAdjust formats, ranges, numbers, and modes.4

Edge Detection KernelselectOptionalType of edge detection kernel to applyIntensitynumberOptionalStrength of the edge detection effect (0.1-5.0)Output FormatselectOptionalOutput format for the processed imageQuality (1-100)numberOptionalOutput quality for lossy formats

TogglesEnable or disable optional behavior.1

Invert EdgescheckboxOptionalEnabled when checkedInvert the edge detection result (black edges on white background)

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Image Edge Detection Kernel

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Key facts

Overview

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Use cases

Examples

1. Extracting Line Art from Photos

2. Isolating Object Boundaries

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