What is a convolution kernel?

A convolution kernel is a small matrix used in image processing to apply effects like blurring, sharpening, or edge detection by calculating the weighted sum of neighboring pixels.

How do I format the kernel values?

Enter your values as a comma-separated list, reading from left to right and top to bottom. For a 3x3 matrix, provide 9 values; for 5x5, provide 25 values.

What does the 'Normalize Kernel' option do?

Normalization ensures the sum of all elements in the matrix equals 1, which prevents the output image from becoming too bright or too dark.

Can I apply this to any image format?

The tool supports common formats including JPEG, PNG, WebP, GIF, BMP, and TIFF.

What is the maximum intensity I can set?

The intensity can be adjusted on a scale from 0.1 to 10.0 to control the impact of the filter.

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Image Custom Convolution Kernel

Apply custom convolution kernel by defining your own matrix values for personalized image effects and processing

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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 custom convolution kernel

ContentPaste or type the main input values.1

Kernel ValuestextareaRequiredMatrix values (left to right, top to bottom). For 3x3: 9 values, for 5x5: 25 values, etc.

SettingsAdjust formats, ranges, numbers, and modes.3

Kernel SizeselectOptionalSize of the convolution matrixIntensitynumberOptionalStrength of the convolution effect (0.1-10.0)Output ModeselectOptionalAdditional processing on the output

TogglesEnable or disable optional behavior.1

Normalize KernelcheckboxOptionalEnabled when checkedNormalize kernel so that sum of all elements equals 1 (except for edge detection)

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Image Custom Convolution Kernel

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Key facts

Overview

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Use cases

Examples

1. Custom Edge Detection

2. Soft Blur Effect

FAQ

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