def test_denoise_tv_float_result_range():
# lena image
img = lena_gray
int_lena = np.multiply(img, 255).astype(np.uint8)
assert np.max(int_lena) > 1
denoised_int_lena = filter.denoise_tv(int_lena, weight=60.0)
# test if the value range of output float data is within [0.0:1.0]
assert denoised_int_lena.dtype == np.float
assert np.max(denoised_int_lena) <= 1.0
assert np.min(denoised_int_lena) >= 0.0
def test_denoise_tv_3d():
"""Apply the TV denoising algorithm on a 3D image representing a sphere."""
x, y, z = np.ogrid[0:40, 0:40, 0:40]
mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
mask = 100 * mask.astype(np.float)
mask += 60
mask += 20 * np.random.random(mask.shape)
mask[mask < 0] = 0
mask[mask > 255] = 255
res = filter.denoise_tv(mask.astype(np.uint8), weight=100)
assert res.dtype == np.float
assert res.std() * 255 < mask.std()
# test wrong number of dimensions
assert_raises(ValueError, filter.denoise_tv, np.random.random((8, 8, 8, 8)))
def test_denoise_tv_2d():
# lena image
img = lena_gray
# add noise to lena
img += 0.5 * img.std() * np.random.random(img.shape)
# clip noise so that it does not exceed allowed range for float images.
img = np.clip(img, 0, 1)
# denoise
denoised_lena = filter.denoise_tv(img, weight=60.0)
# which dtype?
assert denoised_lena.dtype in [np.float, np.float32, np.float64]
from scipy import ndimage
grad = ndimage.morphological_gradient(img, size=((3, 3)))
grad_denoised = ndimage.morphological_gradient(
denoised_lena, size=((3, 3)))
# test if the total variation has decreased
assert grad_denoised.dtype == np.float
assert (np.sqrt((grad_denoised**2).sum())
< np.sqrt((grad**2).sum()) / 2)
from skimage import data, color, img_as_float
from skimage.filter import denoise_tv, denoise_bilateral
lena = img_as_float(data.lena())
lena = lena[220:300, 220:320]
noisy = lena + 0.5 * lena.std() * np.random.random(lena.shape)
noisy = np.clip(noisy, 0, 1)
fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(8, 5))
ax[0, 0].imshow(noisy)
ax[0, 0].axis('off')
ax[0, 0].set_title('noisy')
ax[0, 1].imshow(denoise_tv(noisy, weight=0.1))
ax[0, 1].axis('off')
ax[0, 1].set_title('TV')
ax[0, 2].imshow(denoise_bilateral(noisy, sigma_range=0.03, sigma_spatial=15))
ax[0, 2].axis('off')
ax[0, 2].set_title('Bilateral')
ax[1, 0].imshow(denoise_tv(noisy, weight=0.2))
ax[1, 0].axis('off')
ax[1, 0].set_title('(more) TV')
ax[1, 1].imshow(denoise_bilateral(noisy, sigma_range=0.06, sigma_spatial=15))
ax[1, 1].axis('off')
ax[1, 1].set_title('(more) Bilateral')
ax[1, 2].imshow(lena)
ax[1, 2].axis('off')
ax[1, 2].set_title('original')
