`skimage.restoration.deconvolution:unsupervised_wiener`

source: /dev/scikit-image/src/skimage/restoration/deconvolution.py :143

Signature

   def     unsupervised_wiener (    image    ,    psf    ,    reg    =  None   ,    user_params    =  None   ,    is_real    =  True   ,    clip    =  True   ,  * ,     rng    =  None     )

Summary

Unsupervised Wiener-Hunt deconvolution.

Extended Summary

Return the deconvolution with a Wiener-Hunt approach, where the hyperparameters are automatically estimated. The algorithm is a stochastic iterative process (Gibbs sampler) described in the reference below. See also wiener function.

Parameters

image : ndarray of shape (M, N)

The input degraded image.

psf : ndarray

The impulse response (input image's space) or the transfer function (Fourier space). Both are accepted. The transfer function is automatically recognized as being complex (np.iscomplexobj(psf)).

reg : ndarray, optional

The regularisation operator. The Laplacian by default. It can be an impulse response or a transfer function, as for the psf.

user_params : dict, optional

Dictionary of parameters for the Gibbs sampler. Accepted keys are:

threshold: threshold
burnin: burnin
min_num_iter: min_num_iter
max_num_iter: max_num_iter
callback: callback

clip : bool, optional

True by default. If true, pixel values of the result above 1 or under -1 are thresholded for skimage pipeline compatibility.

rng : {`numpy.random.Generator`, int}, optional

Pseudo-random number generator. By default, a PCG64 generator is used (see numpy.random.default_rng). If rng is an int, it is used to seed the generator.

Returns

x_postmean : ndarray of shape (M, N): The deconvolved image (the posterior mean).
chains : dict: The keys noise and prior contain the chain list of noise and prior precision respectively.

Notes

The estimated image is design as the posterior mean of a probability law (from a Bayesian analysis). The mean is defined as a sum over all the possible images weighted by their respective probability. Given the size of the problem, the exact sum is not tractable. This algorithm use of MCMC to draw image under the posterior law. The practical idea is to only draw highly probable images since they have the biggest contribution to the mean. At the opposite, the less probable images are drawn less often since their contribution is low. Finally, the empirical mean of these samples give us an estimation of the mean, and an exact computation with an infinite sample set.

Examples

from skimage import color, data, restoration
img = color.rgb2gray(data.astronaut())
from scipy.signal import convolve2d
psf = np.ones((5, 5)) / 25
img = convolve2d(img, psf, 'same')
rng = np.random.default_rng()
img += 0.1 * img.std() * rng.standard_normal(img.shape)
deconvolved_img = restoration.unsupervised_wiener(img, psf)

✓

Aliases

skimage.restoration.unsupervised_wiener

Referenced by

This package

release_notes:release_0.14