def test_mean_absolute_error(space):
true = odl.phantom.white_noise(space)
data = odl.phantom.white_noise(space)
result = fom.mean_absolute_error(data, true)
expected = np.mean(np.abs(true - data))
assert result == pytest.approx(expected)
blur = []
false_struct = []
ssim = []
psnr = []
haarpsi = []
# Create mask for ROI to evaluate blurring and false structures. Arbitrarily
# chosen as bone in Shepp-Logan phantom.
mask = (np.asarray(phantom) == 1)
for stddev in np.linspace(0.1, 10, 100):
phantom_noisy = phantom + odl.phantom.white_noise(reco_space,
stddev=stddev)
mse.append(fom.mean_squared_error(phantom_noisy, phantom, normalized=True))
mae.append(fom.mean_absolute_error(phantom_noisy, phantom,
normalized=True))
mvd.append(
fom.mean_value_difference(phantom_noisy, phantom, normalized=True))
std_diff.append(
fom.standard_deviation_difference(phantom_noisy,
phantom,
normalized=True))
range_diff.append(
fom.range_difference(phantom_noisy, phantom, normalized=True))
blur.append(
fom.blurring(phantom_noisy,
phantom,