wiener2

2-D adaptive noise-removal filtering

The syntax wiener2(I,[m n],[mblock nblock],noise) has been removed. Use the wiener2(I,[m n],noise) syntax instead.

Syntax

J = wiener2(I,[m n],noise)

[J,noise_out] = wiener2(I,[m n])

Description

J = wiener2(I,[m n],noise) filters the grayscale image I using a pixel-wise adaptive low-pass Wiener filter. [m n] specifies the size (m-by-n) of the neighborhood used to estimate the local image mean and standard deviation. The additive noise (Gaussian white noise) power is assumed to be noise.

The input image has been degraded by constant power additive noise. wiener2 uses a pixelwise adaptive Wiener method based on statistics estimated from a local neighborhood of each pixel.

[J,noise_out] = wiener2(I,[m n]) returns the estimates of the additive noise power wiener2 calculates before doing the filtering.

Examples

Remove Noise by Adaptive Filtering

This example shows how to use the wiener2 function to apply a Wiener filter (a type of linear filter) to an image adaptively. The Wiener filter tailors itself to the local image variance. Where the variance is large, wiener2 performs little smoothing. Where the variance is small, wiener2 performs more smoothing.

This approach often produces better results than linear filtering. The adaptive filter is more selective than a comparable linear filter, preserving edges and other high-frequency parts of an image. In addition, there are no design tasks; the wiener2 function handles all preliminary computations and implements the filter for an input image. wiener2, however, does require more computation time than linear filtering.

wiener2 works best when the noise is constant-power ("white") additive noise, such as Gaussian noise. The example below applies wiener2 to an image of Saturn with added Gaussian noise.

Read the image into the workspace.

RGB = imread('saturn.png');

Convert the image from truecolor to grayscale.

I = im2gray(RGB);

Add Gaussian noise to the image.

J = imnoise(I,'gaussian',0,0.025);

Display the noisy image. Because the image is quite large, display only a portion of the image.

imshow(J(600:1000,1:600));
title('Portion of the Image with Added Gaussian Noise');

Remove the noise using the wiener2 function.

K = wiener2(J,[5 5]);

Display the processed image. Because the image is quite large, display only a portion of the image.

figure
imshow(K(600:1000,1:600));
title('Portion of the Image with Noise Removed by Wiener Filter');

Input Arguments

I — Input image

2-D numeric array

Input image, specified as a 2-D numeric array.

Data Types: single | double | int16 | uint8 | uint16

[m n] — Neighborhood size

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

Neighborhood size, specified as a 2-element vector of the form [m n] where m is the number of rows and n is the number of columns.

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

noise — Additive noise

numeric array

Additive noise, specified as a numeric array. If you do not specify noise, then wiener2 uses the mean of the local variance, mean2(localVar).

Data Types: single | double

Output Arguments

J — Filtered image

numeric array

Filtered image, returned as a numeric array of the same size and data type as the input image I.

noise_out — Estimate of additive noise power

numeric array

Estimate of additive noise power, returned as a numeric array.

Algorithms

wiener2 estimates the local mean and variance around each pixel.

$$\mu =\frac{1}{NM}{\displaystyle \sum _{n_1,n_2\in \eta }a(n_1,n_2)}$$

and

$${\sigma }^2=\frac{1}{NM}{\displaystyle \sum _{n_1,n_2\in \eta }{a}^2(n_1,n_2)-{\mu }^2},$$

where $$\eta$$ is the N-by-M local neighborhood of each pixel in the image A. wiener2 then creates a pixelwise Wiener filter using these estimates,

$$b(n_1,n_2)=\mu +\frac{{\sigma }^2-{\nu }^2}{{\sigma }^2}(a(n_1,n_2)-\mu ),$$

where ν² is the noise variance. If the noise variance is not given, wiener2 uses the average of all the local estimated variances.

References

[1] Lim, Jae S. Two-Dimensional Signal and Image Processing, Englewood Cliffs, NJ, Prentice Hall, 1990, p. 548, equations 9.44, 9.45, and 9.46.

Extended Capabilities

C/C++ Code Generation

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

wiener2 supports the generation of C and C++ code (requires MATLAB^® Coder™). For more information, see Code Generation for Image Processing.

GPU Arrays

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

This function fully supports GPU arrays. For more information, see Image Processing on a GPU.

Version History

Introduced before R2006a

R2022a: Generate C code using MATLAB Coder

wiener2 now supports the generation of C code (requires MATLAB Coder).

R2021a: Support for GPU acceleration

wiener2 now supports GPU acceleration (requires Parallel Computing Toolbox™).