# imgaussfilt

2-D Gaussian filtering of images

## Syntax

``B = imgaussfilt(A)``
``B = imgaussfilt(A,sigma)``
``B = imgaussfilt(___,Name,Value)``

## Description

````B = imgaussfilt(A)` filters image `A` with a 2-D Gaussian smoothing kernel with standard deviation of 0.5, and returns the filtered image in `B`.You optionally can perform the filtering using a GPU (requires Parallel Computing Toolbox™).```

example

````B = imgaussfilt(A,sigma)` filters image `A` with a 2-D Gaussian smoothing kernel with standard deviation specified by `sigma`.```
````B = imgaussfilt(___,Name,Value)` uses name-value pair arguments to control aspects of the filtering.```

## Examples

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Read image to be filtered.

`I = imread('cameraman.tif');`

Filter the image with a Gaussian filter with standard deviation of 2.

`Iblur = imgaussfilt(I,2);`

Display the original and filtered image in a montage.

```montage({I,Iblur}) title('Original Image (Left) Vs. Gaussian Filtered Image (Right)')```

## Input Arguments

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Image to be filtered, specified as a numeric array of any dimension.

Data Types: `single` | `double` | `int8` | `int16` | `int32` | `uint8` | `uint16` | `uint32`

Standard deviation of the Gaussian distribution, specified as a positive number or a 2-element vector of positive numbers. If you specify a scalar, then `imgaussfilt` uses a square Gaussian kernel.

Data Types: `single` | `double` | `int8` | `int16` | `int32` | `int64` | `uint8` | `uint16` | `uint32` | `uint64`

### Name-Value Pair Arguments

Specify optional comma-separated pairs of `Name,Value` arguments. `Name` is the argument name and `Value` is the corresponding value. `Name` must appear inside quotes. You can specify several name and value pair arguments in any order as `Name1,Value1,...,NameN,ValueN`.

Example: `B = imgaussfilt(A,'FilterSize',3);`

Size of the Gaussian filter, specified as a scalar or 2-element vector of positive, odd integers. If you specify a scalar, then `imgaussfilt` uses a square filter. The default filter size is `2*ceil(2*sigma)+1`.

Data Types: `single` | `double` | `int8` | `int16` | `int32` | `int64` | `uint8` | `uint16` | `uint32` | `uint64`

Image padding, specified as one of the following.

ValueDescription
numeric scalarPad image with elements of constant value.
`'circular'`

Pad with circular repetition of elements within the dimension.

`'replicate'`

Pad by repeating border elements of array.

`'symmetric'`

Pad image with mirror reflections of itself.

Data Types: `single` | `double` | `int8` | `int16` | `int32` | `int64` | `uint8` | `uint16` | `uint32` | `uint64` | `logical` | `char` | `string`

Domain in which to perform filtering, specified as one of the following values:

ValueDescription
`'auto'`Perform convolution in the spatial or frequency domain, based on internal heuristics.
`'frequency'`Perform convolution in the frequency domain.
`'spatial'`Perform convolution in the spatial domain.

Data Types: `char` | `string`

## Output Arguments

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Filtered image, returned as a numeric array of the same class and size as the input image, `A`.

## Tips

• If image `A` contains `Inf`s or `NaN`s, then the behavior of `imgaussfilt` for frequency domain filtering is undefined. This can happen if you set the `'FilterDomain'` parameter to `'frequency'` or if you set it to `'auto'` and `imgaussfilt` uses frequency domain filtering. To restrict the propagation of `Inf`s and `NaN`s in the output in a manner similar to `imfilter`, consider setting the `'FilterDomain'` parameter to `'spatial'`.

• If you set the `'FilterDomain'` parameter to `'auto'`, then `imgaussfilt` uses an internal heuristic to determine whether spatial or frequency domain filtering is faster. This heuristic is machine dependent and may vary for different configurations. For optimal performance, try both options, `'spatial'` and `'frequency'`, to determine the best filtering domain for your image and kernel size.

• If you do not specify the `'Padding'` parameter, then `imgaussfilt` uses `'replicate'` padding by default, which is different from the default used by `imfilter`.

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