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]

Array API Standard Support

wiener has support for Python Array API Standard compatible backends in addition to NumPy. The following combinations of backend and device (or other capability) are supported.

Library	CPU	GPU
NumPy	✅	n/a
CuPy	n/a	✅
PyTorch	✅	⛔
JAX	⚠️ no JIT	⚠️ no JIT
Dask	⚠️ computes graph	n/a

See Support for the array API standard for more information.

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()