def test_sharpness():
# check the edge-preserving nature
S0 = np.ones((30, 30, 30)) * 100
S0[10:20, 10:20, 10:20] = 50
S0[20:30, 20:30, 20:30] = 0
S0_noise = S0 + 20 * np.random.standard_normal((30, 30, 30))
S0n1 = non_local_means(
S0_noise,
sigma=400,
rician=False,
patch_radius=1,
block_radius=1)
edg1 = np.abs(np.mean(S0n1[8, 10:20, 10:20] - S0n1[12, 10:20, 10:20]) - 50)
print("Edge gradient smaller patch", edg1)
S0n2 = non_local_means(
S0_noise,
sigma=400,
rician=False,
patch_radius=2,
block_radius=2)
edg2 = np.abs(np.mean(S0n2[8, 10:20, 10:20] - S0n2[12, 10:20, 10:20]) - 50)
print("Edge gradient larger patch", edg2)
S0n = adaptive_soft_matching(S0, S0n1, S0n2, 400)
edg = np.abs(np.mean(S0n[8, 10:20, 10:20] - S0n[12, 10:20, 10:20]) - 50)
print("Edge gradient ASCM", edg)
assert_(edg2 > edg1)
assert_(edg2 > edg)
assert_(np.abs(edg1 - edg) < 1.5)
def test_sharpness():
# check the edge-preserving nature
S0 = np.ones((30, 30, 30)) * 100
S0[10:20, 10:20, 10:20] = 50
S0[20:30, 20:30, 20:30] = 0
S0_noise = S0 + 20 * np.random.standard_normal((30, 30, 30))
S0n1 = non_local_means(S0_noise,
sigma=400,
rician=False,
patch_radius=1,
block_radius=1)
edg1 = np.abs(np.mean(S0n1[8, 10:20, 10:20] - S0n1[12, 10:20, 10:20]) - 50)
print("Edge gradient smaller patch", edg1)
S0n2 = non_local_means(S0_noise,
sigma=400,
rician=False,
patch_radius=2,
block_radius=2)
edg2 = np.abs(np.mean(S0n2[8, 10:20, 10:20] - S0n2[12, 10:20, 10:20]) - 50)
print("Edge gradient larger patch", edg2)
S0n = adaptive_soft_matching(S0, S0n1, S0n2, 400)
edg = np.abs(np.mean(S0n[8, 10:20, 10:20] - S0n[12, 10:20, 10:20]) - 50)
print("Edge gradient ASCM", edg)
assert_(edg2 > edg1)
assert_(edg2 > edg)
assert_(np.abs(edg1 - edg) < 1.5)
def test_ascm_accuracy():
test_ascm_data_ref = nib.load(dpd.get_data("ascm_test")).get_data()
test_data = nib.load(dpd.get_data("aniso_vox")).get_data()
# the test data was constructed in this manner
mask = test_data > 50
sigma = estimate_sigma(test_data, N=4)
den_small = non_local_means(
test_data,
sigma=sigma,
mask=mask,
patch_radius=1,
block_radius=1,
rician=True)
den_large = non_local_means(
test_data,
sigma=sigma,
mask=mask,
patch_radius=2,
block_radius=1,
rician=True)
S0n = np.array(adaptive_soft_matching(test_data,
den_small, den_large, sigma[0]))
assert_array_almost_equal(S0n, test_ascm_data_ref)
def test_ascm_rmse_with_nlmeans():
# checks the smoothness
S0 = np.ones((30, 30, 30)) * 100
S0[10:20, 10:20, 10:20] = 50
S0[20:30, 20:30, 20:30] = 0
S0_noise = S0 + 20 * np.random.standard_normal((30, 30, 30))
print("Original RMSE", np.sum(np.abs(S0 - S0_noise)) / np.sum(S0))
S0n1 = non_local_means(
S0_noise,
sigma=400,
rician=False,
patch_radius=1,
block_radius=1)
print("Smaller patch RMSE", np.sum(np.abs(S0 - S0n1)) / np.sum(S0))
S0n2 = non_local_means(
S0_noise,
sigma=400,
rician=False,
patch_radius=2,
block_radius=2)
print("Larger patch RMSE", np.sum(np.abs(S0 - S0n2)) / np.sum(S0))
S0n = adaptive_soft_matching(S0, S0n1, S0n2, 400)
print("ASCM RMSE", np.sum(np.abs(S0 - S0n)) / np.sum(S0))
assert_(np.sum(np.abs(S0 - S0n)) / np.sum(S0) <
np.sum(np.abs(S0 - S0n1)) / np.sum(S0))
assert_(np.sum(np.abs(S0 - S0n)) / np.sum(S0) <
np.sum(np.abs(S0 - S0_noise)) / np.sum(S0))
assert_(90 < np.mean(S0n) < 110)
def test_ascm_accuracy():
f_name = dpd.get_fnames("ascm_test")
test_ascm_data_ref = np.asanyarray(nib.load(f_name).dataobj)
test_data = np.asanyarray(nib.load(dpd.get_fnames("aniso_vox")).dataobj)
# the test data was constructed in this manner
mask = test_data > 50
sigma = estimate_sigma(test_data, N=4)
den_small = non_local_means(test_data,
sigma=sigma,
mask=mask,
patch_radius=1,
block_radius=1,
rician=True)
den_large = non_local_means(test_data,
sigma=sigma,
mask=mask,
patch_radius=2,
block_radius=1,
rician=True)
S0n = np.array(
adaptive_soft_matching(test_data, den_small, den_large, sigma[0]))
assert_array_almost_equal(S0n, test_ascm_data_ref)
def test_ascm_rmse_with_nlmeans():
# checks the smoothness
S0 = np.ones((30, 30, 30)) * 100
S0[10:20, 10:20, 10:20] = 50
S0[20:30, 20:30, 20:30] = 0
S0_noise = S0 + 20 * np.random.standard_normal((30, 30, 30))
print("Original RMSE", np.sum(np.abs(S0 - S0_noise)) / np.sum(S0))
S0n1 = non_local_means(S0_noise,
sigma=400,
rician=False,
patch_radius=1,
block_radius=1)
print("Smaller patch RMSE", np.sum(np.abs(S0 - S0n1)) / np.sum(S0))
S0n2 = non_local_means(S0_noise,
sigma=400,
rician=False,
patch_radius=2,
block_radius=2)
print("Larger patch RMSE", np.sum(np.abs(S0 - S0n2)) / np.sum(S0))
S0n = adaptive_soft_matching(S0, S0n1, S0n2, 400)
print("ASCM RMSE", np.sum(np.abs(S0 - S0n)) / np.sum(S0))
assert_(
np.sum(np.abs(S0 - S0n)) / np.sum(S0) < np.sum(np.abs(S0 - S0n1)) /
np.sum(S0))
assert_(
np.sum(np.abs(S0 - S0n)) / np.sum(S0) < np.sum(np.abs(S0 - S0_noise)) /
np.sum(S0))
assert_(90 < np.mean(S0n) < 110)
def test_ascm_static():
S0 = 100 * np.ones((20, 20, 20), dtype='f8')
S0n1 = non_local_means(S0, sigma=0, rician=False,
patch_radius=1, block_radius=1)
S0n2 = non_local_means(S0, sigma=0, rician=False,
patch_radius=2, block_radius=1)
S0n = adaptive_soft_matching(S0, S0n1, S0n2, 0)
assert_array_almost_equal(S0, S0n)
def test_nlmeans_boundary():
# nlmeans preserves boundaries
S0 = 100 + np.zeros((20, 20, 20))
noise = 2 * np.random.standard_normal((20, 20, 20))
S0 += noise
S0[:10, :10, :10] = 300 + noise[:10, :10, :10]
non_local_means(S0, sigma=np.std(noise), rician=False)
assert_(S0[9, 9, 9] > 290)
assert_(S0[10, 10, 10] < 110)
def test_nlmeans_boundary():
# nlmeans preserves boundaries
S0 = 100 + np.zeros((20, 20, 20))
noise = 2 * np.random.standard_normal((20, 20, 20))
S0 += noise
S0[:10, :10, :10] = 300 + noise[:10, :10, :10]
non_local_means(S0, sigma=np.std(noise), rician=False)
assert_(S0[9, 9, 9] > 290)
assert_(S0[10, 10, 10] < 110)
def test_nlmeans_dtype():
S0 = 200 * np.ones((20, 20, 20, 3), dtype='f4')
mask = np.zeros((20, 20, 20))
mask[10:14, 10:14, 10:14] = 1
S0n = non_local_means(S0, sigma=1, mask=mask, rician=True)
assert_equal(S0.dtype, S0n.dtype)
S0 = 200 * np.ones((20, 20, 20), dtype=np.uint16)
mask = np.zeros((20, 20, 20))
mask[10:14, 10:14, 10:14] = 1
S0n = non_local_means(S0, sigma=1, mask=mask, rician=True)
assert_equal(S0.dtype, S0n.dtype)
def test_nlmeans_dtype():
S0 = 200 * np.ones((20, 20, 20, 3), dtype='f4')
mask = np.zeros((20, 20, 20))
mask[10:14, 10:14, 10:14] = 1
S0n = non_local_means(S0, sigma=1, mask=mask, rician=True)
assert_equal(S0.dtype, S0n.dtype)
S0 = 200 * np.ones((20, 20, 20), dtype=np.uint16)
mask = np.zeros((20, 20, 20))
mask[10:14, 10:14, 10:14] = 1
S0n = non_local_means(S0, sigma=1, mask=mask, rician=True)
assert_equal(S0.dtype, S0n.dtype)
def test_ascm_random_noise():
S0 = 100 + 2 * np.random.standard_normal((22, 23, 30))
S0n1 = non_local_means(S0, sigma=1, rician=False,
patch_radius=1, block_radius=1)
S0n2 = non_local_means(S0, sigma=1, rician=False,
patch_radius=2, block_radius=1)
S0n = adaptive_soft_matching(S0, S0n1, S0n2, 1)
print(S0.mean(), S0.min(), S0.max())
print(S0n.mean(), S0n.min(), S0n.max())
assert_(S0n.min() > S0.min())
assert_(S0n.max() < S0.max())
assert_equal(np.round(S0n.mean()), 100)
def test_ascm_static():
S0 = 100 * np.ones((20, 20, 20), dtype='f8')
S0n1 = non_local_means(S0,
sigma=0,
rician=False,
patch_radius=1,
block_radius=1)
S0n2 = non_local_means(S0,
sigma=0,
rician=False,
patch_radius=2,
block_radius=1)
S0n = adaptive_soft_matching(S0, S0n1, S0n2, 0)
assert_array_almost_equal(S0, S0n)
def test_nlmeans_random_noise():
S0 = 100 + 2 * np.random.standard_normal((22, 23, 30))
masker = np.zeros(S0.shape[:3]).astype(bool)
masker[8:15, 8:15, 8:15] = 1
for mask in [None, masker]:
S0nb = non_local_means(S0, sigma=np.std(S0), rician=False, mask=mask)
assert_(S0nb[mask].min() > S0[mask].min())
assert_(S0nb[mask].max() < S0[mask].max())
assert_equal(np.round(S0nb[mask].mean()), 100)
S0nb = non_local_means(S0, sigma=np.std(S0), rician=False, mask=mask)
assert_(S0nb[mask].min() > S0[mask].min())
assert_(S0nb[mask].max() < S0[mask].max())
assert_equal(np.round(S0nb[mask].mean()), 100)
def test_nlmeans_4D_and_mask():
S0 = 200 * np.ones((20, 20, 20, 3), dtype='f8')
mask = np.zeros((20, 20, 20))
mask[10, 10, 10] = 1
S0n = non_local_means(S0, sigma=1, mask=mask, rician=True)
assert_equal(S0.shape, S0n.shape)
assert_equal(np.round(S0n[10, 10, 10]), 200)
assert_equal(S0n[8, 8, 8], 0)
def test_nlmeans_random_noise():
S0 = 100 + 2 * np.random.standard_normal((22, 23, 30))
masker = np.zeros(S0.shape[:3]).astype(bool)
masker[8:15, 8:15, 8:15] = 1
for mask in [None, masker]:
S0nb = non_local_means(S0, sigma=np.std(S0), rician=False, mask=mask)
assert_(S0nb[mask].min() > S0[mask].min())
assert_(S0nb[mask].max() < S0[mask].max())
assert_equal(np.round(S0nb[mask].mean()), 100)
S0nb = non_local_means(S0, sigma=np.std(S0), rician=False, mask=mask)
assert_(S0nb[mask].min() > S0[mask].min())
assert_(S0nb[mask].max() < S0[mask].max())
assert_equal(np.round(S0nb[mask].mean()), 100)
def test_nlmeans_4D_and_mask():
S0 = 200 * np.ones((20, 20, 20, 3), dtype='f8')
mask = np.zeros((20, 20, 20))
mask[10, 10, 10] = 1
S0n = non_local_means(S0, sigma=1, mask=mask, rician=True)
assert_equal(S0.shape, S0n.shape)
assert_equal(np.round(S0n[10, 10, 10]), 200)
assert_equal(S0n[8, 8, 8], 0)
def test_ascm_random_noise():
S0 = 100 + 2 * np.random.standard_normal((22, 23, 30))
S0n1 = non_local_means(S0,
sigma=1,
rician=False,
patch_radius=1,
block_radius=1)
S0n2 = non_local_means(S0,
sigma=1,
rician=False,
patch_radius=2,
block_radius=1)
S0n = adaptive_soft_matching(S0, S0n1, S0n2, 1)
print(S0.mean(), S0.min(), S0.max())
print(S0n.mean(), S0n.min(), S0n.max())
assert_(S0n.min() > S0.min())
assert_(S0n.max() < S0.max())
assert_equal(np.round(S0n.mean()), 100)
def test_nlmeans_random_noise():
S0 = 100 + 2 * np.random.standard_normal((22, 23, 30))
S0nb = non_local_means(S0,
sigma=np.ones((22, 23, 30)) * np.std(S0),
rician=False)
print(S0.mean(), S0.min(), S0.max())
print(S0nb.mean(), S0nb.min(), S0nb.max())
assert_(S0nb.min() > S0.min())
assert_(S0nb.max() < S0.max())
assert_equal(np.round(S0nb.mean()), 100)
def test_nlmeans_static():
S0 = 100 * np.ones((20, 20, 20), dtype='f8')
S0nb = non_local_means(S0, sigma=1.0, rician=False)
assert_array_almost_equal(S0, S0nb)
``non_local_means`` denoising. For ``non_local_means`` first we need to
estimate the standard deviation of the noise. We use N=4 since the Sherbrooke
dataset was acquired on a 1.5T Siemens scanner with a 4 array head coil.
"""
sigma = estimate_sigma(data, N=4)
"""
For the denoised version of the original data which preserves sharper features,
we perform non-local means with smaller patch size.
"""
den_small = non_local_means(
data,
sigma=sigma,
mask=mask,
patch_radius=1,
block_radius=1,
rician=True)
"""
For the denoised version of the original data that implies more smoothing, we
perform non-local means with larger patch size.
"""
den_large = non_local_means(
data,
sigma=sigma,
mask=mask,
patch_radius=2,
block_radius=1,
"""
In order to call ``non_local_means`` first you need to estimate the standard
deviation of the noise. We use N=4 since the Sherbrooke dataset was acquired
on a 1.5T Siemens scanner with a 4 array head coil.
"""
sigma = estimate_sigma(data, N=4)
t = time()
"""
Calling the main function ``non_local_means``
"""
den = non_local_means(data,
sigma=sigma,
mask=mask,
patch_radius=1,
block_radius=1,
rician=True)
print("total time", time() - t)
t = time()
den = nlmeans(data,
sigma=sigma,
mask=mask,
patch_radius=1,
block_radius=1,
rician=True)
print("total time", time() - t)
"""
def test_nlmeans_static():
S0 = 100 * np.ones((20, 20, 20), dtype='f8')
S0nb = non_local_means(S0, sigma=1.0, rician=False)
assert_array_almost_equal(S0, S0nb)
``non_local_means`` denoising. For ``non_local_means`` first we need to
estimate the standard deviation of the noise. We use N=4 since the Sherbrooke
dataset was acquired on a 1.5T Siemens scanner with a 4 array head coil.
"""
sigma = estimate_sigma(data, N=4)
"""
For the denoised version of the original data which preserves sharper features,
we perform non-local means with smaller patch size.
"""
den_small = non_local_means(
data,
sigma=sigma,
mask=mask,
patch_radius=1,
block_radius=1,
rician=True)
"""
For the denoised version of the original data that implies more smoothing, we
perform non-local means with larger patch size.
"""
den_large = non_local_means(
data,
sigma=sigma,
mask=mask,
patch_radius=2,
block_radius=1,
deviation of the noise. We use N=4 since the Sherbrooke dataset was acquired
on a 1.5T Siemens scanner with a 4 array head coil.
"""
sigma = estimate_sigma(data, N=4)
t = time()
"""
Calling the main function ``non_local_means``
"""
den = non_local_means(
data,
sigma=sigma,
mask=mask,
patch_radius=1,
block_radius=1,
rician=True)
print("total time", time() - t)
t = time()
den = nlmeans(data, sigma=sigma, mask=mask, patch_radius= 1, block_radius = 1, rician= True)
print("total time", time() - t)
"""
Let us plot the axial slice of the denoised output
"""
axial_middle = data.shape[2] / 2
