medfilt2

2-D median filtering

Syntax

J = medfilt2(I)

J = medfilt2(I,[m n])

J = medfilt2(___,padopt)

Description

J = medfilt2(I) performs median filtering of the image I in two dimensions. Each output pixel contains the median value in a 3-by-3 neighborhood around the corresponding pixel in the input image.

J = medfilt2(I,[m n]) performs median filtering, where each output pixel contains the median value in the m-by-n neighborhood around the corresponding pixel in the input image.

J = medfilt2(___,padopt) controls how medfilt2 pads the image boundaries.

Examples

Remove Salt and Pepper Noise from Image

Read image into workspace and display it.

I = imread('eight.tif');
figure, imshow(I)

Add salt and pepper noise.

J = imnoise(I,'salt & pepper',0.02);

Use a median filter to filter out the noise.

K = medfilt2(J);

Display results, side-by-side.

imshowpair(J,K,'montage')

Input Arguments

I — Input image

2-D grayscale image | 2-D binary image

Input image, specified as a 2-D grayscale or binary image.

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

[m n] — Neighborhood size

[3 3] (default) | 2-element vector

Neighborhood size, specified as a 2-element vector of positive integers.

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

padopt — Padding option

'zeros' (default) | 'symmetric' | 'indexed'

Padding option, specified as one of the following values.

Value	Description
'zeros' (default)	Pad the image with 0s.
'symmetric'	Symmetrically extend the image at the boundaries.
'indexed'	If the class of I is double, then pad the image with 1s; otherwise, pad with 0s.

Data Types: char | string

Output Arguments

J — Output image

numeric matrix

Output image, returned as a numeric matrix of the same class as the input image I.

Tips

• Median filtering is a nonlinear operation often used in image processing to reduce "salt and pepper" noise. A median filter is more effective than convolution when the goal is to simultaneously reduce noise and preserve edges. For information about performance considerations, see ordfilt2.

• If the input image I is of an integer class, then all the output values are returned as integers. If the number of pixels in the neighborhood (m*n) is even, then some of the median values might not be integers. In these cases, the fractional parts are discarded. Logical input is treated similarly. For example, the true median for the following 2-by-2 neighborhood in a uint8 array is 4.5, but medfilt2 discards the fractional part and returns 4.

  1 5
  4 8

• If you specify padopt as 'zeros' or 'indexed', then the padding can skew the median near the image boundary. Pixels within one-half the width of the neighborhood ([m n]/2) of the edges can appear distorted.

Algorithms

On the CPU, medfilt2 uses ordfilt2 to perform the filtering.

References

[1] Lim, Jae S., Two-Dimensional Signal and Image Processing, Englewood Cliffs, NJ, Prentice Hall, 1990, pp. 469-476.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• medfilt2 supports the generation of C code (requires MATLAB^® Coder™). Note that if you choose the generic MATLAB Host Computer target platform, medfilt2 generates code that uses a precompiled, platform-specific shared library. Use of a shared library preserves performance optimizations but limits the target platforms for which code can be generated. For more information, see Types of Code Generation Support in Image Processing Toolbox.

• When generating code, the padopt argument must be a compile-time constant.

GPU Code Generation

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

Usage notes and limitations:

• When generating code, the padopt argument must be a compile-time constant.

GPU Arrays

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

Usage notes and limitations:

• Padding options, specified through the padopt argument, are not supported on the GPU.

• If you perform median filtering using a GPU, then the neighborhood [m n] must be square with odd-length sides between 3 and 15.

For more information, see Image Processing on a GPU.