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Convolution calculator FullScreen

A convolution calculator is a tool that performs the mathematical operation called convolution. Convolution is a fundamental concept in mathematics and signal processing that combines two functions to produce a third function.

Enter first data sequence:
Enter second data sequence:
Result data sequence:


What is Convolution

Convolution is a mathematical operation that combines two functions to produce a third function. It is often used in signal processing, image processing, and other fields of mathematics and engineering.

In simple terms, convolution can be thought of as a way of "blending" or "mixing" two functions together. It involves sliding one function (often referred to as the "input" or "signal") over another function (often called the "kernel" or "filter"), multiplying corresponding values at each position, and then summing the results.

Mathematically, the convolution of two continuous functions, f(x) and g(x), is denoted as (f * g)(x) and is defined as:

(f * g)(x) = ∫[−∞, +∞] f(t)g(x-t)dt

In the case of discrete functions or sequences, convolution is defined as the sum of products of corresponding elements:

(f * g)[n] = Σ[k = -∞ to +∞] f[k]g[n-k]

Convolution has several important properties and applications. Some common uses of convolution include:

  1. Filtering: Convolution is widely used in signal processing to apply filters to signals. By convolving a signal with a filter kernel, certain frequency components can be enhanced or suppressed.

  2. Image Processing: Convolution is used for various image processing operations like blurring, edge detection, and sharpening. Image filters, such as Gaussian filters or Sobel operators, are commonly implemented using convolution.

  3. System Analysis: Convolution is employed in the analysis of linear time-invariant systems in fields such as control theory and electrical engineering. It helps determine how a system responds to different inputs.

  4. Probability and Statistics: Convolution plays a crucial role in probability theory and statistics, particularly when dealing with the sum or product of random variables.

Convolution is a fundamental concept with wide-ranging applications. Understanding and utilizing convolution allows for the manipulation, analysis, and transformation of functions and signals in various disciplines.

Convolution calculation

The sequence y(n) is equal to the convolution of sequences x(n) and h(n):

y(n)=x(n)*h(n)=\sum_{k=-\infty }^{\infty }x(k)h(n-k)

For finite sequences x(n) with M values and h(n) with H values:

y(n)=\sum_{k=0 }^{N }x(n+k)h(N-1-k)

For N = 0 .. M+H-2

Convolution calculation Example

Consider the following 5x5 input matrix:

1 2 3 4 5
6 7 8 9 10
11 12 13 14 15
16 17 18 19 20
21 22 23 24 25

And let's use the following 3x3 kernel matrix for convolution:

1 0 1
0 -1 0
1 0 1

To compute the convolution, we will perform element-wise multiplication of the kernel with the corresponding region of the input matrix and sum up the results.

Here's how the calculation is done for the top-left corner (1,1) of the output matrix:

1 2 3
6 7 8
11 12 13

Element-wise multiplication:

(1 * 1) + (2 * 0) + (3 * 1) + (6 * 0) + (7 * -1) + (8 * 0) + (11 * 1) + (12 * 0) + (13 * 1) = 2

So, the top-left value of the output matrix will be 2.

Similarly, you can perform the convolution calculation for each region of the input matrix to obtain the complete output matrix.

Note: The size of the output matrix will be smaller than the input matrix, as it depends on the size of the kernel and the padding applied (if any). The above example assumes no padding is applied.