`scipy.signal._signaltools:wiener`

source: /scipy/signal/_signaltools.py :1656

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]

Array API Standard Support

wiener has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. 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 dev-arrayapi for more information.

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()

✓

Aliases

scipy.signal.wiener