skimage.restoration.denoise_nl_means(image, patch_size=7, patch_distance=11, h=0.1, multichannel=False, fast_mode=True, sigma=0.0, *, preserve_range=None) [source]
 
Perform non-local means denoising on 2-D or 3-D grayscale images, and 2-D RGB images.  Parameters 
 
image2D or 3D ndarray 

Input image to be denoised, which can be 2D or 3D, and grayscale or RGB (for 2D images only, see multichannel parameter).  
patch_sizeint, optional 

Size of patches used for denoising.  
patch_distanceint, optional 

Maximal distance in pixels where to search patches used for denoising.  
hfloat, optional 

Cut-off distance (in gray levels). The higher h, the more permissive one is in accepting patches. A higher h results in a smoother image, at the expense of blurring features. For a Gaussian noise of standard deviation sigma, a rule of thumb is to choose the value of h to be sigma of slightly less.  
multichannelbool, optional 

Whether the last axis of the image is to be interpreted as multiple channels or another spatial dimension.  
fast_modebool, optional 

If True (default value), a fast version of the non-local means algorithm is used. If False, the original version of non-local means is used. See the Notes section for more details about the algorithms.  
sigmafloat, optional 

The standard deviation of the (Gaussian) noise. If provided, a more robust computation of patch weights is computed that takes the expected noise variance into account (see Notes below).  
preserve_rangebool, optional 

Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float. Also see https://scikit-image.org/docs/dev/user_guide/data_types.html    Returns 
 
resultndarray 

Denoised image, of same shape as image.     Notes The non-local means algorithm is well suited for denoising images with specific textures. The principle of the algorithm is to average the value of a given pixel with values of other pixels in a limited neighbourhood, provided that the patches centered on the other pixels are similar enough to the patch centered on the pixel of interest. In the original version of the algorithm [1], corresponding to fast=False, the computational complexity is: image.size * patch_size ** image.ndim * patch_distance ** image.ndim
 Hence, changing the size of patches or their maximal distance has a strong effect on computing times, especially for 3-D images. However, the default behavior corresponds to fast_mode=True, for which another version of non-local means [2] is used, corresponding to a complexity of: image.size * patch_distance ** image.ndim
 The computing time depends only weakly on the patch size, thanks to the computation of the integral of patches distances for a given shift, that reduces the number of operations [1]. Therefore, this algorithm executes faster than the classic algorithm (fast_mode=False), at the expense of using twice as much memory. This implementation has been proven to be more efficient compared to other alternatives, see e.g. [3]. Compared to the classic algorithm, all pixels of a patch contribute to the distance to another patch with the same weight, no matter their distance to the center of the patch. This coarser computation of the distance can result in a slightly poorer denoising performance. Moreover, for small images (images with a linear size that is only a few times the patch size), the classic algorithm can be faster due to boundary effects. The image is padded using the reflect mode of skimage.util.pad before denoising. If the noise standard deviation, sigma, is provided a more robust computation of patch weights is used. Subtracting the known noise variance from the computed patch distances improves the estimates of patch similarity, giving a moderate improvement to denoising performance [4]. It was also mentioned as an option for the fast variant of the algorithm in [3]. When sigma is provided, a smaller h should typically be used to avoid oversmoothing. The optimal value for h depends on the image content and noise level, but a reasonable starting point is h = 0.8 * sigma when fast_mode is True, or h = 0.6 * sigma when fast_mode is False. References  
1(1,2)  
A. Buades, B. Coll, & J-M. Morel. A non-local algorithm for image denoising. In CVPR 2005, Vol. 2, pp. 60-65, IEEE. DOI:10.1109/CVPR.2005.38  
2  
J. Darbon, A. Cunha, T.F. Chan, S. Osher, and G.J. Jensen, Fast nonlocal filtering applied to electron cryomicroscopy, in 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008, pp. 1331-1334. DOI:10.1109/ISBI.2008.4541250  
3(1,2)  
Jacques Froment. Parameter-Free Fast Pixelwise Non-Local Means Denoising. Image Processing On Line, 2014, vol. 4, pp. 300-326. DOI:10.5201/ipol.2014.120  
4  
A. Buades, B. Coll, & J-M. Morel. Non-Local Means Denoising. Image Processing On Line, 2011, vol. 1, pp. 208-212. DOI:10.5201/ipol.2011.bcm_nlm   Examples >>> a = np.zeros((40, 40))
>>> a[10:-10, 10:-10] = 1.
>>> a += 0.3 * np.random.randn(*a.shape)
>>> denoised_a = denoise_nl_means(a, 7, 5, 0.1)