def test_wavelet_threshold():
rstate = np.random.RandomState(1234)
img = astro_gray
sigma = 0.1
noisy = img + sigma * rstate.randn(*(img.shape))
noisy = np.clip(noisy, 0, 1)
# employ a single, user-specified threshold instead of BayesShrink sigmas
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
denoised = _wavelet_threshold(noisy,
wavelet='db1',
method=None,
threshold=sigma)
psnr_noisy = peak_signal_noise_ratio(img, noisy)
psnr_denoised = peak_signal_noise_ratio(img, denoised)
assert_(psnr_denoised > psnr_noisy)
# either method or threshold must be defined
with testing.raises(ValueError):
_wavelet_threshold(noisy, wavelet='db1', method=None, threshold=None)
# warns if a threshold is provided in a case where it would be ignored
with expected_warnings(
["Thresholding method ", PYWAVELET_ND_INDEXING_WARNING]):
_wavelet_threshold(noisy,
wavelet='db1',
method='BayesShrink',
threshold=sigma)
def test_wavelet_threshold():
rstate = np.random.RandomState(1234)
img = astro_gray
sigma = 0.1
noisy = img + sigma * rstate.randn(*(img.shape))
noisy = np.clip(noisy, 0, 1)
# employ a single, user-specified threshold instead of BayesShrink sigmas
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
denoised = _wavelet_threshold(noisy, wavelet='db1', method=None,
threshold=sigma)
psnr_noisy = compare_psnr(img, noisy)
psnr_denoised = compare_psnr(img, denoised)
assert_(psnr_denoised > psnr_noisy)
# either method or threshold must be defined
with testing.raises(ValueError):
_wavelet_threshold(noisy, wavelet='db1', method=None, threshold=None)
# warns if a threshold is provided in a case where it would be ignored
with expected_warnings(["Thresholding method ",
PYWAVELET_ND_INDEXING_WARNING]):
_wavelet_threshold(noisy, wavelet='db1', method='BayesShrink',
threshold=sigma)
def test_wavelet_threshold():
rstate = np.random.RandomState(1234)
img = astro_gray
sigma = 0.1
noisy = img + sigma * rstate.randn(*(img.shape))
noisy = np.clip(noisy, 0, 1)
# employ a single, uniform threshold instead of BayesShrink sigmas
denoised = _wavelet_threshold(noisy, wavelet='db1', threshold=sigma)
psnr_noisy = compare_psnr(img, noisy)
psnr_denoised = compare_psnr(img, denoised)
assert_(psnr_denoised > psnr_noisy)
def test_wavelet_threshold():
rstate = np.random.RandomState(1234)
img = astro_gray
sigma = 0.1
noisy = img + sigma * rstate.randn(*(img.shape))
noisy = np.clip(noisy, 0, 1)
# employ a single, uniform threshold instead of BayesShrink sigmas
denoised = _wavelet_threshold(noisy, wavelet='db1', threshold=sigma)
psnr_noisy = compare_psnr(img, noisy)
psnr_denoised = compare_psnr(img, denoised)
assert_(psnr_denoised > psnr_noisy)
def test_wavelet_threshold():
rstate = np.random.default_rng(1234)
img = astro_gray
sigma = 0.1
noisy = img + sigma * rstate.standard_normal(img.shape)
noisy = np.clip(noisy, 0, 1)
# employ a single, user-specified threshold instead of BayesShrink sigmas
denoised = _wavelet_threshold(noisy, wavelet='db1', method=None,
threshold=sigma)
psnr_noisy = peak_signal_noise_ratio(img, noisy)
psnr_denoised = peak_signal_noise_ratio(img, denoised)
assert psnr_denoised > psnr_noisy
# either method or threshold must be defined
with pytest.raises(ValueError):
_wavelet_threshold(noisy, wavelet='db1', method=None, threshold=None)
# warns if a threshold is provided in a case where it would be ignored
with expected_warnings(["Thresholding method "]):
_wavelet_threshold(noisy, wavelet='db1', method='BayesShrink',
threshold=sigma)
