conv2

2-D convolution

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Syntax

C = conv2(A,B)

C = conv2(u,v,A)

C = conv2(___,shape)

Description

C = conv2(A,B) returns the two-dimensional convolution of matrices A and B.

If A is a matrix and B is a row vector (or A is a row vector and B is a matrix), then C is the convolution of each row of the matrix with the vector.
If A is a matrix and B is a column vector (or A is a column vector and B is a matrix), then C is the convolution of each column of the matrix with the vector.

example

C = conv2(u,v,A) first convolves each column of A with the vector u, and then it convolves each row of the result with the vector v. This behavior applies regardless of whether u or v is a row or column vector.

example

C = conv2(___,shape) returns a subsection of the convolution according to shape. For example, C = conv2(A,B,"same") returns the central part of the convolution, which is the same size as A.

example

Examples

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2-D Convolution

Open Live Script

In applications such as image processing, it can be useful to compare the input of a convolution directly to the output. The conv2 function allows you to control the size of the output.

Create a 3-by-3 random matrix A and a 4-by-4 random matrix B. Compute the full convolution of A and B, which is a 6-by-6 matrix.

A = rand(3);
B = rand(4);
Cfull = conv2(A,B)

Cfull = 6×6

    0.7861    1.2768    1.4581    1.0007    0.2876    0.0099
    1.0024    1.8458    3.0844    2.5151    1.5196    0.2560
    1.0561    1.9824    3.5790    3.9432    2.9708    0.7587
    1.6790    2.0772    3.0052    3.7511    2.7593    1.5129
    0.9902    1.1000    2.4492    1.6082    1.7976    1.2655
    0.1215    0.1469    1.0409    0.5540    0.6941    0.6499

Compute the central part of the convolution Csame, which is a submatrix of Cfull with the same size as A. Csame is equal to Cfull(3:5,3:5).

Csame = conv2(A,B,"same")

Csame = 3×3

    3.5790    3.9432    2.9708
    3.0052    3.7511    2.7593
    2.4492    1.6082    1.7976

Extract 2-D Pedestal Edges

Open Live Script

The Sobel edge-finding operation uses a 2-D convolution to detect edges in images and other 2-D data.

Create and plot a 2-D pedestal with interior height equal to one.

A = zeros(10);
A(3:7,3:7) = ones(5);
mesh(A)

Figure contains an axes object. The axes object contains an object of type surface.

Convolve the rows of A with the vector u, and then convolve the rows of the result with the vector v. The convolution extracts the horizontal edges of the pedestal.

u = [1 0 -1]';
v = [1 2 1];
Ch = conv2(u,v,A);
mesh(Ch)

Figure contains an axes object. The axes object contains an object of type surface.

To extract the vertical edges of the pedestal, reverse the order of convolution with u and v.

Cv = conv2(v,u,A);
mesh(Cv)

Figure contains an axes object. The axes object contains an object of type surface.

Compute and plot the combined edges of the pedestal.

figure
mesh(sqrt(Ch.^2 + Cv.^2))

Figure contains an axes object. The axes object contains an object of type surface.

Input Arguments

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`A` — Input array
vector | matrix

Input array, specified as a vector or matrix.

`B` — Second input array
vector | matrix

Second input array, specified as a vector or a matrix to convolve with A. The array B does not have to be the same size as A.

`u` — Input vector
row or column vector

Input vector, specified as a row or column vector. u convolves with each column of A.

`v` — Second input vector
row or column vector

Second input vector, specified as a row or column vector. v convolves with each row of the convolution of u with the columns of A.

`shape` — Subsection of convolution
`"full"` (default) | `"same"` | `"valid"`

Subsection of the convolution, specified as one of these values:

"full" — Return the full 2-D convolution.
"same" — Return the central part of the convolution, which is the same size as A.
"valid" — Return only parts of the convolution that are computed without zero-padded edges.

Output Arguments

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`C` — 2-D convolution
vector | matrix

2-D convolution, returned as a vector or matrix. When A and B are matrices, then the convolution C = conv2(A,B) has size size(A)+size(B)-1. When [m,n] = size(A), p = length(u), and q = length(v), then the convolution C = conv2(u,v,A) has m+p-1 rows and n+q-1 columns.

When one or more input arguments to conv2 are of type single, then the output is of type single. Otherwise, conv2 converts inputs to type double and returns type double.

Data Types: double | single

More About

collapse all

2-D Convolution

For discrete, two-dimensional matrices A and B, the following equation defines the convolution of A and B:

$C (j, k) = \sum_{p} \sum_{q} A (p, q) B (j - p + 1, k - q + 1)$

p and q run over all values that lead to legal subscripts of A(p,q) and B(j-p+1,k-q+1).

Using this definition, conv2 calculates the direct convolution of two matrices, rather than the FFT-based convolution.

Extended Capabilities

Tall Arrays
Calculate with arrays that have more rows than fit in memory.

The conv2 function supports tall arrays with the following usage notes and limitations:

If shape is "full" (default), then the inputs A and B must not be empty and only one them can be a tall array.
If shape is "same" or "valid", then B cannot be a tall array.
u and v cannot be tall arrays.

For more information, see Tall Arrays.

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Thread-Based Environment
Run code in the background using MATLAB® `backgroundPool` or accelerate code with Parallel Computing Toolbox™ `ThreadPool`.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

The conv2 function fully supports GPU arrays. To run the function on a GPU, specify the input data as a gpuArray (Parallel Computing Toolbox). For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Distributed Arrays
Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

Usage notes and limitations:

Input vectors u and v must not be distributed arrays.

For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).

Version History

Introduced before R2006a

conv2

Syntax

Description

Examples

2-D Convolution

Extract 2-D Pedestal Edges

Input Arguments

A — Input array vector | matrix

B — Second input array vector | matrix

u — Input vector row or column vector

v — Second input vector row or column vector

shape — Subsection of convolution "full" (default) | "same" | "valid"

Output Arguments

C — 2-D convolution vector | matrix

More About

2-D Convolution

Extended Capabilities

Tall Arrays Calculate with arrays that have more rows than fit in memory.

C/C++ Code Generation Generate C and C++ code using MATLAB® Coder™.

GPU Code Generation Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Thread-Based Environment Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

GPU Arrays Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Distributed Arrays Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

Version History

See Also

`A` — Input array
vector | matrix

`B` — Second input array
vector | matrix

`u` — Input vector
row or column vector

`v` — Second input vector
row or column vector

`shape` — Subsection of convolution
`"full"` (default) | `"same"` | `"valid"`

`C` — 2-D convolution
vector | matrix

Tall Arrays
Calculate with arrays that have more rows than fit in memory.

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Thread-Based Environment
Run code in the background using MATLAB® `backgroundPool` or accelerate code with Parallel Computing Toolbox™ `ThreadPool`.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Distributed Arrays
Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.