def test_input_image_not_modified():
input_image = noisy_img.copy()
parameter_ranges = {'sigma': np.random.random(5) / 2}
calibrate_denoiser(input_image,
_denoise_wavelet,
denoise_parameters=parameter_ranges)
assert_(np.all(noisy_img == input_image))
def test_calibrate_denoiser():
parameter_ranges = {'sigma': np.linspace(0.1, 1, 5) / 2}
denoiser = calibrate_denoiser(noisy_img,
_denoise_wavelet,
denoise_parameters=parameter_ranges)
denoised_mse = mse(denoiser(noisy_img), test_img)
original_mse = mse(noisy_img, test_img)
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))
# Parameters to test when calibrating the denoising algorithm
sigma_range = np.arange(0.05, 0.5, 0.05)
parameter_ranges = {
'sigma': np.arange(0.1, 0.3, 0.02),
'wavelet': ['db1', 'db2'],
'convert2ycbcr': [True, False],
'multichannel': [True]
}
# Denoised image using default parameters of `denoise_wavelet`
default_output = denoise_wavelet(noisy, multichannel=True, rescale_sigma=True)
# Calibrate denoiser
calibrated_denoiser = calibrate_denoiser(noisy,
_denoise_wavelet,
denoise_parameters=parameter_ranges)
# Denoised image using calibrated denoiser
calibrated_output = calibrated_denoiser(noisy)
fig, axes = plt.subplots(1, 3, sharey=True, figsize=(15, 5))
for ax, img, title in zip(
axes, [noisy, default_output, calibrated_output],
['Noisy Image', 'Denoised (Default)', 'Denoised (Calibrated)']):
ax.imshow(img)
ax.set_title(title)
ax.set_yticks([])
ax.set_xticks([])
# Parameters to test when calibrating the denoising algorithm
sigma_range = np.arange(0.05, 0.5, 0.05)
parameter_ranges = {
'sigma': np.arange(0.1, 0.3, 0.02),
'wavelet': ['db1', 'db2'],
'convert2ycbcr': [True, False],
'multichannel': [True]
}
# Denoised image using default parameters of `denoise_wavelet`
default_output = denoise_wavelet(noisy, multichannel=True, rescale_sigma=True)
# Calibrate denoiser
calibrated_denoiser = calibrate_denoiser(noisy,
_denoise_wavelet,
denoise_parameters=parameter_ranges)
# Denoised image using calibrated denoiser
calibrated_output = calibrated_denoiser(noisy)
fig, axes = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(15, 5))
for ax, img, title in zip(
axes, [noisy, default_output, calibrated_output],
['Noisy Image', 'Denoised (Default)', 'Denoised (Calibrated)']):
ax.imshow(img)
ax.set_title(title)
ax.set_yticks([])
ax.set_xticks([])
