`scipy.signal._signaltools:wiener`

source: build-install/usr/lib/python3.14/site-packages/scipy/signal/_signaltools.py:1707

Signature

def wiener(im, mysize = None, noise = None)

Summary

Perform a Wiener filter on an N-dimensional array.

Extended Summary

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

Parameters

im : ndarray: An N-dimensional array.
mysize : int 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.
noise : float, optional: The noise-power to use. If None, then noise is estimated as the average of the local variance of the input.

Returns

out : ndarray: Wiener filtered result with the same shape as im.

Notes

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

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)

✗

<matplotlib.image.AxesImage object at 0x7f3f6879d450>

plt.show()

✓

Aliases

scipy.signal.wiener