Source code for porespy.filters._nlmeans

import numpy as np
from skimage.restoration import denoise_nl_means, estimate_sigma
from skimage.exposure import rescale_intensity, match_histograms
import dask
# from dask.diagnostics import ProgressBar

[docs]def nl_means_layered(im, cores=None, axis=0, patch_size=5, patch_distance=15, h=4): r""" Apply the non-local means filter to each 2D layer of a stack in parallel. This applies ``skimage.restoration.denoise_nl_means`` to each slice, so refer to the documentation of that function for further information. Parameters ---------- im : ndarray The greyscale image with noise to be removed cores : int (optional) The number of cores to use for the processing. By default all available cores are used. axis : int The axis along which slices should be taken. This should correspond to the axis of rotation of the tomography stage, so if the sample was rotated about the z-axis, then use ``axis=2``. patch_size : int Size of patches used for denoising patch_distance : int Maximal distance in pixels where to search patches used for denoising. h : float 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. Notes ----- The quality of the result depends on ``patch_size``, ``patch_distance``, ``h``, and ``sigma``. It is recommended to experiment with a single slice first until a suitable set of parameters is found. Each slice in the stack is adjusted to have the same histogram and intensity. Examples -------- `Click here <>`_ to view online example. """ @dask.delayed def apply_func(func, **kwargs): return func(**kwargs) temp = np.copy(im) for i in range(im.shape[2]): temp[:, :, i] = match_histograms(temp[:, :, i], temp[:, :, 0], multichannel=False) p2, p98 = np.percentile(temp, (2, 98)) temp = rescale_intensity(temp, in_range=(p2, p98)) temp = temp / temp.max() sigma_est = np.mean(estimate_sigma(temp[:, :, 0], multichannel=False)) kw = {'image': temp, 'patch_size': patch_size, 'patch_distance': patch_distance, 'h': h * sigma_est, 'multichannel': False, 'fast_mode': True} temp = np.swapaxes(temp, 0, axis) results = [] for i in range(im.shape[2]): layer = temp[i, ...] kw["image"] = layer t = apply_func(func=denoise_nl_means, **kw) results.append(t) # with ProgressBar(): # ims = dask.compute(results, num_workers=cores)[0] ims = dask.compute(results, num_workers=cores)[0] result = np.array(ims) result = np.swapaxes(result, 0, axis) return result