.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/filters/plot_deconvolution.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_deconvolution.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_deconvolution.py:


=====================
Image Deconvolution
=====================
In this example, we deconvolve an image using Richardson-Lucy
deconvolution algorithm ([1]_, [2]_).

The algorithm is based on a PSF (Point Spread Function),
where PSF is described as the impulse response of the
optical system. The blurred image is sharpened through a number of
iterations, which needs to be hand-tuned.

.. [1] William Hadley Richardson, "Bayesian-Based Iterative
       Method of Image Restoration",
       J. Opt. Soc. Am. A 27, 1593-1607 (1972), :DOI:`10.1364/JOSA.62.000055`

.. [2] https://en.wikipedia.org/wiki/Richardson%E2%80%93Lucy_deconvolution

.. GENERATED FROM PYTHON SOURCE LINES 19-58



.. image-sg:: /auto_examples/filters/images/sphx_glr_plot_deconvolution_001.png
   :alt: Original Data, Noisy data, Restoration using Richardson-Lucy
   :srcset: /auto_examples/filters/images/sphx_glr_plot_deconvolution_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import numpy as np
    import matplotlib.pyplot as plt

    from scipy.signal import convolve2d as conv2

    from skimage import color, data, restoration

    rng = np.random.default_rng()

    astro = color.rgb2gray(data.astronaut())

    psf = np.ones((5, 5)) / 25
    astro = conv2(astro, psf, 'same')
    # Add Noise to Image
    astro_noisy = astro.copy()
    astro_noisy += (rng.poisson(lam=25, size=astro.shape) - 10) / 255.0

    # Restore Image using Richardson-Lucy algorithm
    deconvolved_RL = restoration.richardson_lucy(astro_noisy, psf, num_iter=30)

    fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(8, 5))
    plt.gray()

    for a in (ax[0], ax[1], ax[2]):
        a.axis('off')

    ax[0].imshow(astro)
    ax[0].set_title('Original Data')

    ax[1].imshow(astro_noisy)
    ax[1].set_title('Noisy data')

    ax[2].imshow(deconvolved_RL, vmin=astro_noisy.min(), vmax=astro_noisy.max())
    ax[2].set_title('Restoration using\nRichardson-Lucy')


    fig.subplots_adjust(wspace=0.02, hspace=0.2, top=0.9, bottom=0.05, left=0, right=1)
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.611 seconds)


.. _sphx_glr_download_auto_examples_filters_plot_deconvolution.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_deconvolution.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_