.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/filters/plot_restoration.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_filters_plot_restoration.py>`
        to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_filters_plot_restoration.py:


=====================
Image Deconvolution
=====================

In this example, we deconvolve a noisy version of an image using Wiener
and unsupervised Wiener algorithms. These algorithms are based on
linear models that can't restore sharp edge as much as non-linear
methods (like TV restoration) but are much faster.

Wiener filter
-------------
The inverse filter based on the PSF (Point Spread Function),
the prior regularization (penalisation of high frequency) and the
tradeoff between the data and prior adequacy. The regularization
parameter must be hand tuned.

Unsupervised Wiener
-------------------
This algorithm has a self-tuned regularization parameters based on
data learning. This is not common and based on the following
publication [1]_. The algorithm is based on an iterative Gibbs sampler that
draw alternatively samples of posterior conditional law of the image,
the noise power and the image frequency power.

.. [1] François Orieux, Jean-François Giovannelli, and Thomas
       Rodet, "Bayesian estimation of regularization and point
       spread function parameters for Wiener-Hunt deconvolution",
       J. Opt. Soc. Am. A 27, 1593-1607 (2010)
       https://www.osapublishing.org/josaa/abstract.cfm?URI=josaa-27-7-1593
       https://hal.archives-ouvertes.fr/hal-00674508

.. GENERATED FROM PYTHON SOURCE LINES 33-65



.. image-sg:: /auto_examples/filters/images/sphx_glr_plot_restoration_001.png
   :alt: Data, Self tuned restoration
   :srcset: /auto_examples/filters/images/sphx_glr_plot_restoration_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import numpy as np
    import matplotlib.pyplot as plt

    from skimage import color, data, restoration

    rng = np.random.default_rng()

    astro = color.rgb2gray(data.astronaut())
    from scipy.signal import convolve2d as conv2

    psf = np.ones((5, 5)) / 25
    astro = conv2(astro, psf, 'same')
    astro += 0.1 * astro.std() * rng.standard_normal(astro.shape)

    deconvolved, _ = restoration.unsupervised_wiener(astro, psf)

    fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 5), sharex=True, sharey=True)

    plt.gray()

    ax[0].imshow(astro, vmin=deconvolved.min(), vmax=deconvolved.max())
    ax[0].axis('off')
    ax[0].set_title('Data')

    ax[1].imshow(deconvolved)
    ax[1].axis('off')
    ax[1].set_title('Self tuned restoration')

    fig.tight_layout()

    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.566 seconds)


.. _sphx_glr_download_auto_examples_filters_plot_restoration.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-image/scikit-image/v0.23.2?filepath=notebooks/auto_examples/filters/plot_restoration.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_restoration.ipynb <plot_restoration.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_restoration.py <plot_restoration.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

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