Parameters
Input Matrix (A)
Result Matrix (k · A)
Operating the Convolution Engine
Get your convolved matrix in three simple steps.
Define Input Matrix
Set the dimensions and values for your main input matrix. This could represent an image, a sensor reading, or any 2D data.
Set the Kernel
Input the values for the convolution kernel (or filter). This smaller matrix determines the effect you want to apply.
Analyze the Output
The resulting feature map (the convolved matrix) is calculated and displayed instantly, showing the result of the filtering operation.
What is Matrix Convolution?
The fundamental process behind image filtering and neural networks.
Matrix convolution is an operation where a smaller matrix, called a kernel or filter, slides over a larger input matrix. At each position, an element-wise multiplication between the kernel and the overlapping section of the input matrix is performed, and the results are summed up to produce a single value in the output matrix (or feature map).
This "sliding window" process allows the kernel to extract specific features from the input data. For example:
- An averaging kernel creates a blur effect.
- A kernel that emphasizes differences between neighbors can sharpen an image or detect edges.
Our convolution matrix calculator provides a hands-on way to understand this powerful and essential operation.
A Feature-Rich Signal Processor
Features designed for experimentation and deep understanding.
Dynamic Matrix Sizing
Effortlessly change the dimensions of your input matrix and kernel. The calculator's interface instantly adapts to your needs.
Live Operation Visualization
Watch the kernel slide over the input matrix and see the output being calculated step-by-step, providing an unparalleled intuitive understanding.
Preset Kernel Library
Instantly load common kernels for effects like Gaussian blur, sharpen, emboss, and Sobel edge detection to see their effects immediately.
Applications of Convolution
Beyond simple filters, convolution is a cornerstone of modern tech.
Image Processing
The primary application. Used for blurring, sharpening, edge detection, noise reduction, and many other essential image manipulation techniques.
Deep Learning (CNNs)
Convolutional Neural Networks use learnable kernels to automatically detect features in data, forming the basis of modern computer vision.
Signal Processing
In 1D, convolution is used to apply filters to audio signals or time-series data, for tasks like smoothing or modeling system responses (e.g., reverb).
