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Image Custom Convolution Kernel

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

Click to upload file or drag and drop file here

Maximum file size: 10MB Supported formats: image/jpeg, image/png, image/webp, image/gif, image/bmp, image/tiff

Image to apply custom convolution kernel

Size of the convolution matrix

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

Strength of the convolution effect (0.1-10.0)

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

Additional processing on the output

Key Facts

Category
Design
Input Types
file, select, textarea, number, checkbox
Output Type
file
Sample Coverage
4
API Ready
Yes

Overview

The Image Custom Convolution Kernel tool allows you to apply precise mathematical filters to your images by defining custom matrix values, enabling advanced image processing and unique visual effects.

When to Use

  • •When you need to perform custom image sharpening, blurring, or edge detection.
  • •When you want to experiment with specific mathematical kernels for computer vision research.
  • •When you need to apply specialized artistic filters that standard image editors do not provide.

How It Works

  • •Upload your source image in a supported format like JPEG, PNG, or WebP.
  • •Select the matrix size (3x3, 5x5, or 7x7) and input your custom numerical values.
  • •Adjust the intensity and normalization settings to control the strength and balance of the effect.
  • •Choose an optional output mode, such as grayscale or threshold, and process the image.

Use Cases

Creating custom artistic filters for digital photography.
Applying specific mathematical kernels for image enhancement or noise reduction.
Generating edge-detected versions of images for computer vision preprocessing.

Examples

1. Custom Edge Detection

Computer Vision Researcher
Background
Need to isolate structural outlines from a set of architectural photographs for analysis.
Problem
Standard filters are too generic and do not capture the specific edge details required.
How to Use
Upload the image, select a 3x3 matrix, and input a Laplacian edge detection kernel.
Example Config
kernelSize: 3, kernelValues: -1, -1, -1, -1, 8, -1, -1, -1, -1, normalize: false
Outcome
The tool outputs an image highlighting the edges of the architecture, ready for further analysis.

2. Soft Blur Effect

Graphic Designer
Background
Creating a dreamy, soft-focus background for a website banner.
Problem
Built-in blur tools are too aggressive and lack the specific texture control needed.
How to Use
Upload the image, select a 3x3 matrix, and input a box blur kernel.
Example Config
kernelSize: 3, kernelValues: 1, 1, 1, 1, 1, 1, 1, 1, 1, normalize: true, intensity: 1.0
Outcome
A smoothly blurred image with a consistent, soft aesthetic suitable for web design.

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FAQ

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.

API Documentation

Request Endpoint

POST /en/api/tools/image-custom-kernel

Request Parameters

Parameter Name Type Required Description
imageFile file (Upload required) Yes Image to apply custom convolution kernel
kernelSize select No Size of the convolution matrix
kernelValues textarea Yes Matrix values (left to right, top to bottom). For 3x3: 9 values, for 5x5: 25 values, etc.
intensity number No Strength of the convolution effect (0.1-10.0)
normalize checkbox No Normalize kernel so that sum of all elements equals 1 (except for edge detection)
outputMode select No Additional processing on the output

File type parameters need to be uploaded first via POST /upload/image-custom-kernel to get filePath, then pass filePath to the corresponding file field.

Response Format

{
  "filePath": "/public/processing/randomid.ext",
  "fileName": "output.ext",
  "contentType": "application/octet-stream",
  "size": 1024,
  "metadata": {
    "key": "value"
  },
  "error": "Error message (optional)",
  "message": "Notification message (optional)"
}
File: File

AI MCP Documentation

Add this tool to your MCP server configuration: 

{
  "mcpServers": {
    "elysiatools-image-custom-kernel": {
      "name": "image-custom-kernel",
      "description": "Apply custom convolution kernel by defining your own matrix values for personalized image effects and processing",
      "baseUrl": "https://elysiatools.com/mcp/sse?toolId=image-custom-kernel",
      "command": "",
      "args": [],
      "env": {},
      "isActive": true,
      "type": "sse"
    }
  }
}

You can chain multiple tools, e.g.: `https://elysiatools.com/mcp/sse?toolId=png-to-webp,jpg-to-webp,gif-to-webp`, max 20 tools.

Supports URL file links or Base64 encoding for file parameters.

If you encounter any issues, please contact us at [email protected]