# Denoise images using Non-Local Means (NLMEANS)

Using the non-local means filter [Coupe08] and [Coupe11] and you can denoise 3D or 4D images and boost the SNR of your datasets. You can also decide between modeling the noise as Gaussian or Rician (default).

import numpy as np
import matplotlib.pyplot as plt
from time import time
from dipy.denoise.nlmeans import nlmeans
from dipy.denoise.noise_estimate import estimate_sigma
from dipy.data import get_fnames

t1_fname = get_fnames('stanford_t1')

print("vol size", data.shape)


In order to call non_local_means first you need to estimate the standard deviation of the noise. We use N=32 since the Stanford dataset was acquired on a 3T GE scanner with a 32 array head coil.

sigma = estimate_sigma(data, N=32)


Calling the main function non_local_means

t = time()

print("total time", time() - t)


Let us plot the axial slice of the denoised output

axial_middle = data.shape // 2

before = data[:, :, axial_middle].T
after = den[:, :, axial_middle].T

difference = np.abs(after.astype(np.float64) - before.astype(np.float64))

fig, ax = plt.subplots(1, 3)
ax.imshow(before, cmap='gray', origin='lower')
ax.set_title('before')
ax.imshow(after, cmap='gray', origin='lower')
ax.set_title('after')
ax.imshow(difference, cmap='gray', origin='lower')
ax.set_title('difference')

plt.savefig('denoised.png', bbox_inches='tight') Showing axial slice before (left) and after (right) NLMEANS denoising

save_nifti('denoised.nii.gz', den, affine)


An improved version of non-local means denoising is adaptive soft coefficient matching, please refer to example_denoise_ascm for more details.