def test_invariant_denoise_color():
denoised_img_color = _invariant_denoise(
noisy_img_color,
_denoise_wavelet,
denoiser_kwargs=dict(multichannel=True))
denoised_mse = mse(denoised_img_color, test_img_color)
original_mse = mse(noisy_img_color, test_img_color)
assert_(denoised_mse < original_mse)
def test_invariant_denoise_color(dtype):
denoised_img_color = _invariant_denoise(
noisy_img_color.astype(dtype),
_denoise_wavelet,
denoiser_kwargs=dict(channel_axis=-1))
denoised_mse = mse(denoised_img_color, test_img_color)
original_mse = mse(noisy_img_color, test_img_color)
assert denoised_mse < original_mse
assert denoised_img_color.dtype == _supported_float_type(dtype)
def test_invariant_denoise_color_deprecated():
with expected_warnings(["`multichannel` is a deprecated argument"]):
denoised_img_color = _invariant_denoise(
noisy_img_color,
_denoise_wavelet,
denoiser_kwargs=dict(multichannel=True))
denoised_mse = mse(denoised_img_color, test_img_color)
original_mse = mse(noisy_img_color, test_img_color)
assert_(denoised_mse < original_mse)
def test_calibrate_denoiser_extra_output():
parameter_ranges = {'sigma': np.linspace(0.1, 1, 5) / 2}
_, (parameters_tested,
losses) = calibrate_denoiser(noisy_img,
_denoise_wavelet,
denoise_parameters=parameter_ranges,
extra_output=True)
all_denoised = [
_invariant_denoise(noisy_img,
_denoise_wavelet,
denoiser_kwargs=denoiser_kwargs)
for denoiser_kwargs in parameters_tested
]
ground_truth_losses = [mse(img, test_img) for img in all_denoised]
assert_(np.argmin(losses) == np.argmin(ground_truth_losses))
def test_invariant_denoise_3d():
denoised_img_3d = _invariant_denoise(noisy_img_3d, _denoise_wavelet)
denoised_mse = mse(denoised_img_3d, test_img_3d)
original_mse = mse(noisy_img_3d, test_img_3d)
assert_(denoised_mse < original_mse)
def test_invariant_denoise():
denoised_img = _invariant_denoise(noisy_img, _denoise_wavelet)
denoised_mse = mse(denoised_img, test_img)
original_mse = mse(noisy_img, test_img)
assert_(denoised_mse < original_mse)
# blue line) have the same shape and the same minimizer.
#
from skimage.restoration.j_invariant import _invariant_denoise
sigma_range = np.arange(sigma / 2, 1.5 * sigma, 0.025)
parameters_tested = [{
'sigma': sigma,
'convert2ycbcr': True,
'wavelet': 'db2',
'multichannel': True
} for sigma in sigma_range]
denoised_invariant = [
_invariant_denoise(noisy, _denoise_wavelet, denoiser_kwargs=params)
for params in parameters_tested
]
self_supervised_loss = [mse(img, noisy) for img in denoised_invariant]
ground_truth_loss = [mse(img, image) for img in denoised_invariant]
opt_idx = np.argmin(self_supervised_loss)
plot_idx = [0, opt_idx, len(sigma_range) - 1]
get_inset = lambda x: x[25:225, 100:300]
plt.figure(figsize=(10, 12))
gs = gridspec.GridSpec(3, 3)
ax1 = plt.subplot(gs[0, :])
