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test_noise_estimate.py
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test_noise_estimate.py
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from __future__ import division, print_function
import numpy as np
import nibabel as nib
from numpy.testing import (assert_almost_equal, assert_equal, assert_,
assert_array_almost_equal)
from dipy.denoise.noise_estimate import _inv_nchi_cdf, piesno, estimate_sigma
from dipy.denoise.noise_estimate import _piesno_3D
import dipy.data
# See page 5 of the reference paper for tested values
def test_inv_nchi():
# Values taken from hispeed.MedianPIESNO.lambdaPlus
# and hispeed.MedianPIESNO.lambdaMinus
N = 8
K = 20
alpha = 0.01
lambdaMinus = _inv_nchi_cdf(N, K, alpha/2)
lambdaPlus = _inv_nchi_cdf(N, K, 1 - alpha/2)
assert_almost_equal(lambdaMinus, 6.464855180579397)
assert_almost_equal(lambdaPlus, 9.722849086419043)
def test_piesno():
# Values taken from hispeed.OptimalPIESNO with the test data
# in the package computed in matlab
test_piesno_data = nib.load(dipy.data.get_data("test_piesno")).get_data()
sigma = piesno(test_piesno_data, N=8, alpha=0.01, l=1, eps=1e-10,
return_mask=False)
assert_almost_equal(sigma, 0.010749458025559)
noise1 = (np.random.randn(100, 100, 100) * 50) + 10
noise2 = (np.random.randn(100, 100, 100) * 50) + 10
rician_noise = np.sqrt(noise1**2 + noise2**2)
sigma, mask = piesno(rician_noise, N=1, alpha=0.01, l=1, eps=1e-10,
return_mask=True)
# less than 3% of error?
assert_(np.abs(sigma - 50) / sigma < 0.03)
# Test using the median as the initial estimation
initial_estimation = (np.median(sigma) /
np.sqrt(2 * _inv_nchi_cdf(1, 1, 0.5)))
sigma, mask = _piesno_3D(rician_noise, N=1, alpha=0.01, l=1, eps=1e-10,
return_mask=True,
initial_estimation=initial_estimation)
assert_(np.abs(sigma - 50) / sigma < 0.03)
sigma = _piesno_3D(rician_noise, N=1, alpha=0.01, l=1, eps=1e-10,
return_mask=False,
initial_estimation=initial_estimation)
assert_(np.abs(sigma - 50) / sigma < 0.03)
sigma = _piesno_3D(np.zeros_like(rician_noise), N=1, alpha=0.01, l=1,
eps=1e-10, return_mask=False,
initial_estimation=initial_estimation)
assert_(np.all(sigma == 0))
sigma, mask = _piesno_3D(np.zeros_like(rician_noise), N=1, alpha=0.01, l=1,
eps=1e-10, return_mask=True,
initial_estimation=initial_estimation)
assert_(np.all(sigma == 0))
assert_(np.all(mask == 0))
# Check if no noise points found in array it exits
sigma = _piesno_3D(1000*np.ones_like(rician_noise), N=1, alpha=0.01, l=1,
eps=1e-10, return_mask=False, initial_estimation=10)
assert_(np.all(sigma == 10))
def test_estimate_sigma():
sigma = estimate_sigma(np.ones((7, 7, 7)), disable_background_masking=True)
assert_equal(sigma, 0.)
sigma = estimate_sigma(np.ones((7, 7, 7, 3)),
disable_background_masking=True)
assert_equal(sigma, np.array([0., 0., 0.]))
sigma = estimate_sigma(5 * np.ones((7, 7, 7)),
disable_background_masking=False)
assert_equal(sigma, 0.)
sigma = estimate_sigma(5 * np.ones((7, 7, 7, 3)),
disable_background_masking=False)
assert_equal(sigma, np.array([0., 0., 0.]))
arr = np.zeros((3, 3, 3))
arr[0, 0, 0] = 1
sigma = estimate_sigma(arr, disable_background_masking=False, N=1)
assert_array_almost_equal(sigma,
(0.10286889997472792 /
np.sqrt(0.42920367320510366)))
arr = np.zeros((3, 3, 3, 3))
arr[0, 0, 0] = 1
sigma = estimate_sigma(arr, disable_background_masking=False, N=1)
assert_array_almost_equal(sigma,
np.array([0.10286889997472792 /
np.sqrt(0.42920367320510366),
0.10286889997472792 /
np.sqrt(0.42920367320510366),
0.10286889997472792 /
np.sqrt(0.42920367320510366)]))
arr = np.zeros((3, 3, 3))
arr[0, 0, 0] = 1
sigma = estimate_sigma(arr, disable_background_masking=True, N=4)
assert_array_almost_equal(sigma, 0.46291005 / np.sqrt(0.4834941393603609))
arr = np.zeros((3, 3, 3))
arr[0, 0, 0] = 1
sigma = estimate_sigma(arr, disable_background_masking=True, N=0)
assert_array_almost_equal(sigma, 0.46291005 / np.sqrt(1))
arr = np.zeros((3, 3, 3, 3))
arr[0, 0, 0] = 1
sigma = estimate_sigma(arr, disable_background_masking=True, N=12)
assert_array_almost_equal(sigma,
np.array([0.46291005 /
np.sqrt(0.4946862482541263),
0.46291005 /
np.sqrt(0.4946862482541263),
0.46291005 /
np.sqrt(0.4946862482541263)]))