def test_invariant_denoise():
# denoised_img = _invariant_denoise(noisy_img, _denoise_wavelet)
denoised_img = _invariant_denoise(noisy_img, denoise_tv_chambolle)
denoised_mse = mse(denoised_img, test_img)
original_mse = mse(noisy_img, test_img)
assert denoised_mse < original_mse
def test_calibrate_denoiser_tv():
parameter_ranges = {"weight": np.linspace(0.01, 0.4, 10)}
denoiser = calibrate_denoiser(noisy_img,
denoise_tv_chambolle,
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():
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_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_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
