scipy.signal.

wiener#

scipy.signal.wiener(im, mysize=None, noise=None)[source]#

Perform a Wiener filter on an N-dimensional array.

Apply a Wiener filter to the N-dimensional array im.

Parameters:

imndarray: An N-dimensional array.
mysizeint or array_like, optional: A scalar or an N-length list giving the size of the Wiener filter window in each dimension. Elements of mysize should be odd. If mysize is a scalar, then this scalar is used as the size in each dimension.
noisefloat, optional: The noise-power to use. If None, then noise is estimated as the average of the local variance of the input.

Returns:

outndarray: Wiener filtered result with the same shape as im.

Notes

This implementation is similar to wiener2 in Matlab/Octave. For more details see [1]

References

[1]

Lim, Jae S., Two-Dimensional Signal and Image Processing, Englewood Cliffs, NJ, Prentice Hall, 1990, p. 548.

Examples

>>> from scipy.datasets import face
>>> from scipy.signal import wiener
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> rng = np.random.default_rng()
>>> img = rng.random((40, 40))    #Create a random image
>>> filtered_img = wiener(img, (5, 5))  #Filter the image
>>> f, (plot1, plot2) = plt.subplots(1, 2)
>>> plot1.imshow(img)
>>> plot2.imshow(filtered_img)
>>> plt.show()