wiener(im, mysize=None, noise=None)
Apply a Wiener filter to the N-dimensional array :None:None:`im`.
This implementation is similar to wiener2 in Matlab/Octave. For more details see
An N-dimensional array.
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.
The noise-power to use. If None, then noise is estimated as the average of the local variance of the input.
Wiener filtered result with the same shape as :None:None:`im`.
Perform a Wiener filter on an N-dimensional array.
>>> from scipy.misc 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()
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.signal._signaltools.wiener
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