.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/filters/plot_nonlocal_means.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` 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_nonlocal_means.py: ================================================= Non-local means denoising for preserving textures ================================================= In this example, we denoise a detail of the astronaut image using the non-local means filter. The non-local means algorithm replaces the value of a pixel by an average of a selection of other pixels values: small patches centered on the other pixels are compared to the patch centered on the pixel of interest, and the average is performed only for pixels that have patches close to the current patch. As a result, this algorithm can restore well textures, that would be blurred by other denoising algorithm. When the ``fast_mode`` argument is ``False``, a spatial Gaussian weighting is applied to the patches when computing patch distances. When ``fast_mode`` is ``True`` a faster algorithm employing uniform spatial weighting on the patches is applied. For either of these cases, if the noise standard deviation, ``sigma``, is provided, the expected noise variance is subtracted out when computing patch distances. This can lead to a modest improvement in image quality. The ``estimate_sigma`` function can provide a good starting point for setting the ``h`` (and optionally, ``sigma``) parameters for the non-local means algorithm. ``h`` is a constant that controls the decay in patch weights as a function of the distance between patches. Larger ``h`` allows more smoothing between disimilar patches. In this demo, ``h``, was hand-tuned to give the approximate best-case performance of each variant. .. GENERATED FROM PYTHON SOURCE LINES 33-110 .. image-sg:: /auto_examples/filters/images/sphx_glr_plot_nonlocal_means_001.png :alt: noisy, non-local means (slow), non-local means (slow, using $\sigma_{est}$), original (noise free), non-local means (fast), non-local means (fast, using $\sigma_{est}$) :srcset: /auto_examples/filters/images/sphx_glr_plot_nonlocal_means_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none estimated noise standard deviation = 0.07868034086869481 PSNR (noisy) = 22.20 PSNR (slow) = 29.44 PSNR (slow, using sigma) = 29.84 PSNR (fast) = 29.05 PSNR (fast, using sigma) = 29.41 | .. code-block:: Python import numpy as np import matplotlib.pyplot as plt from skimage import data, img_as_float from skimage.restoration import denoise_nl_means, estimate_sigma from skimage.metrics import peak_signal_noise_ratio from skimage.util import random_noise astro = img_as_float(data.astronaut()) astro = astro[30:180, 150:300] sigma = 0.08 noisy = random_noise(astro, var=sigma**2) # estimate the noise standard deviation from the noisy image sigma_est = np.mean(estimate_sigma(noisy, channel_axis=-1)) print(f'estimated noise standard deviation = {sigma_est}') patch_kw = dict( patch_size=5, patch_distance=6, channel_axis=-1 # 5x5 patches # 13x13 search area ) # slow algorithm denoise = denoise_nl_means(noisy, h=1.15 * sigma_est, fast_mode=False, **patch_kw) # slow algorithm, sigma provided denoise2 = denoise_nl_means( noisy, h=0.8 * sigma_est, sigma=sigma_est, fast_mode=False, **patch_kw ) # fast algorithm denoise_fast = denoise_nl_means(noisy, h=0.8 * sigma_est, fast_mode=True, **patch_kw) # fast algorithm, sigma provided denoise2_fast = denoise_nl_means( noisy, h=0.6 * sigma_est, sigma=sigma_est, fast_mode=True, **patch_kw ) fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(8, 6), sharex=True, sharey=True) ax[0, 0].imshow(noisy) ax[0, 0].axis('off') ax[0, 0].set_title('noisy') ax[0, 1].imshow(denoise) ax[0, 1].axis('off') ax[0, 1].set_title('non-local means\n(slow)') ax[0, 2].imshow(denoise2) ax[0, 2].axis('off') ax[0, 2].set_title('non-local means\n(slow, using $\\sigma_{est}$)') ax[1, 0].imshow(astro) ax[1, 0].axis('off') ax[1, 0].set_title('original\n(noise free)') ax[1, 1].imshow(denoise_fast) ax[1, 1].axis('off') ax[1, 1].set_title('non-local means\n(fast)') ax[1, 2].imshow(denoise2_fast) ax[1, 2].axis('off') ax[1, 2].set_title('non-local means\n(fast, using $\\sigma_{est}$)') fig.tight_layout() # print PSNR metric for each case psnr_noisy = peak_signal_noise_ratio(astro, noisy) psnr = peak_signal_noise_ratio(astro, denoise) psnr2 = peak_signal_noise_ratio(astro, denoise2) psnr_fast = peak_signal_noise_ratio(astro, denoise_fast) psnr2_fast = peak_signal_noise_ratio(astro, denoise2_fast) print(f'PSNR (noisy) = {psnr_noisy:0.2f}') print(f'PSNR (slow) = {psnr:0.2f}') print(f'PSNR (slow, using sigma) = {psnr2:0.2f}') print(f'PSNR (fast) = {psnr_fast:0.2f}') print(f'PSNR (fast, using sigma) = {psnr2_fast:0.2f}') plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.949 seconds) .. _sphx_glr_download_auto_examples_filters_plot_nonlocal_means.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/v0.24.0?filepath=notebooks/auto_examples/filters/plot_nonlocal_means.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_nonlocal_means.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_nonlocal_means.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_