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


===============================
Fill in defects with inpainting
===============================

Inpainting [1]_ is the process of reconstructing lost or deteriorated
parts of images and videos.

The reconstruction (restoration) is performed in an automatic way by
exploiting the information present in non-damaged regions.

In this example, we show how the masked pixels get inpainted using an
inpainting algorithm based on the biharmonic equation [2]_ [3]_ [4]_.

.. [1]  Wikipedia. Inpainting
        https://en.wikipedia.org/wiki/Inpainting
.. [2]  Wikipedia. Biharmonic equation
        https://en.wikipedia.org/wiki/Biharmonic_equation
.. [3]  S.B.Damelin and N.S.Hoang. "On Surface Completion and Image
        Inpainting by Biharmonic Functions: Numerical Aspects",
        International Journal of Mathematics and Mathematical Sciences,
        Vol. 2018, Article ID 3950312
        :DOI:`10.1155/2018/3950312`
.. [4]  C. K. Chui and H. N. Mhaskar, MRA Contextual-Recovery Extension of
        Smooth Functions on Manifolds, Appl. and Comp. Harmonic Anal.,
        28 (2010), 104-113,
        :DOI:`10.1016/j.acha.2009.04.004`

.. GENERATED FROM PYTHON SOURCE LINES 29-88



.. image-sg:: /auto_examples/filters/images/sphx_glr_plot_inpaint_001.png
   :alt: Original image, Mask, Defected image, Inpainted image
   :srcset: /auto_examples/filters/images/sphx_glr_plot_inpaint_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import numpy as np
    import matplotlib.pyplot as plt

    from skimage import data
    from skimage.morphology import disk, binary_dilation
    from skimage.restoration import inpaint

    image_orig = data.astronaut()

    # Create mask with six block defect regions
    mask = np.zeros(image_orig.shape[:-1], dtype=bool)
    mask[20:60, 0:20] = 1
    mask[160:180, 70:155] = 1
    mask[30:60, 170:195] = 1
    mask[-60:-30, 170:195] = 1
    mask[-180:-160, 70:155] = 1
    mask[-60:-20, 0:20] = 1

    # Add a few long, narrow defects
    mask[200:205, -200:] = 1
    mask[150:255, 20:23] = 1
    mask[365:368, 60:130] = 1

    # Add randomly positioned small point-like defects
    rstate = np.random.default_rng(0)
    for radius in [0, 2, 4]:
        # larger defects are less common
        thresh = 3 + 0.25 * radius  # make larger defects less common
        tmp_mask = rstate.standard_normal(image_orig.shape[:-1]) > thresh
        if radius > 0:
            tmp_mask = binary_dilation(tmp_mask, disk(radius, dtype=bool))
        mask[tmp_mask] = 1

    # Apply defect mask to the image over the same region in each color channel
    image_defect = image_orig * ~mask[..., np.newaxis]

    image_result = inpaint.inpaint_biharmonic(image_defect, mask, channel_axis=-1)

    fig, axes = plt.subplots(ncols=2, nrows=2)
    ax = axes.ravel()

    ax[0].set_title('Original image')
    ax[0].imshow(image_orig)

    ax[1].set_title('Mask')
    ax[1].imshow(mask, cmap=plt.cm.gray)

    ax[2].set_title('Defected image')
    ax[2].imshow(image_defect)

    ax[3].set_title('Inpainted image')
    ax[3].imshow(image_result)

    for a in ax:
        a.axis('off')

    fig.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.517 seconds)


.. _sphx_glr_download_auto_examples_filters_plot_inpaint.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_inpaint.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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