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


====================
Denoising a picture
====================

In this example, we denoise a noisy version of a picture using the total
variation, bilateral, and wavelet denoising filters.

Total variation and bilateral algorithms typically produce "posterized" images
with flat domains separated by sharp edges. It is possible to change the degree
of posterization by controlling the tradeoff between denoising and faithfulness
to the original image.

Total variation filter
----------------------

The result of this filter is an image that has a minimal total variation norm,
while being as close to the initial image as possible. The total variation is
the L1 norm of the gradient of the image.

Bilateral filter
----------------

A bilateral filter is an edge-preserving and noise reducing filter. It averages
pixels based on their spatial closeness and radiometric similarity.

Wavelet denoising filter
------------------------

A wavelet denoising filter relies on the wavelet representation of the image.
The noise is represented by small values in the wavelet domain which are set to
0.

In color images, wavelet denoising is typically done in the `YCbCr color
space`_ as denoising in separate color channels may lead to more apparent
noise.

.. _`YCbCr color space`: https://en.wikipedia.org/wiki/YCbCr

.. GENERATED FROM PYTHON SOURCE LINES 41-104



.. image-sg:: /auto_examples/filters/images/sphx_glr_plot_denoise_001.png
   :alt: Noisy, TV, Bilateral, Wavelet denoising, Original, (more) TV, (more) Bilateral, Wavelet denoising in YCbCr colorspace
   :srcset: /auto_examples/filters/images/sphx_glr_plot_denoise_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Estimated Gaussian noise standard deviation = 0.1491090647998956
    Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).






|

.. code-block:: Python


    import matplotlib.pyplot as plt

    from skimage.restoration import (
        denoise_tv_chambolle,
        denoise_bilateral,
        denoise_wavelet,
        estimate_sigma,
    )
    from skimage import data, img_as_float
    from skimage.util import random_noise


    original = img_as_float(data.chelsea()[100:250, 50:300])

    sigma = 0.155
    noisy = random_noise(original, var=sigma**2)

    fig, ax = plt.subplots(nrows=2, ncols=4, figsize=(8, 5), sharex=True, sharey=True)

    plt.gray()

    # Estimate the average noise standard deviation across color channels.
    sigma_est = estimate_sigma(noisy, channel_axis=-1, average_sigmas=True)
    # Due to clipping in random_noise, the estimate will be a bit smaller than the
    # specified sigma.
    print(f'Estimated Gaussian noise standard deviation = {sigma_est}')

    ax[0, 0].imshow(noisy)
    ax[0, 0].axis('off')
    ax[0, 0].set_title('Noisy')
    ax[0, 1].imshow(denoise_tv_chambolle(noisy, weight=0.1, channel_axis=-1))
    ax[0, 1].axis('off')
    ax[0, 1].set_title('TV')
    ax[0, 2].imshow(
        denoise_bilateral(noisy, sigma_color=0.05, sigma_spatial=15, channel_axis=-1)
    )
    ax[0, 2].axis('off')
    ax[0, 2].set_title('Bilateral')
    ax[0, 3].imshow(denoise_wavelet(noisy, channel_axis=-1, rescale_sigma=True))
    ax[0, 3].axis('off')
    ax[0, 3].set_title('Wavelet denoising')

    ax[1, 1].imshow(denoise_tv_chambolle(noisy, weight=0.2, channel_axis=-1))
    ax[1, 1].axis('off')
    ax[1, 1].set_title('(more) TV')
    ax[1, 2].imshow(
        denoise_bilateral(noisy, sigma_color=0.1, sigma_spatial=15, channel_axis=-1)
    )
    ax[1, 2].axis('off')
    ax[1, 2].set_title('(more) Bilateral')
    ax[1, 3].imshow(
        denoise_wavelet(noisy, channel_axis=-1, convert2ycbcr=True, rescale_sigma=True)
    )
    ax[1, 3].axis('off')
    ax[1, 3].set_title('Wavelet denoising\nin YCbCr colorspace')
    ax[1, 0].imshow(original)
    ax[1, 0].axis('off')
    ax[1, 0].set_title('Original')

    fig.tight_layout()

    plt.show()


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

   **Total running time of the script:** (0 minutes 8.501 seconds)


.. _sphx_glr_download_auto_examples_filters_plot_denoise.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_denoise.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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