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TST: 2d and 3d. DOC: adds refs to doc

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Scott Sievert
2016-07-16 12:09:52 -05:00
parent 643fe77840
commit 5300db1e8d
2 changed files with 22 additions and 15 deletions
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skimage/restoration/_denoise.py
+11 -4
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@@ -344,11 +344,11 @@ def _wavelet_threshold(img, wavelet, threshold=None, sigma=None, mode='soft'):
of the denoised image.
wavelet : string
The type of wavelet to perform. Can be any of the options
[pywt.wavelist]_ outputs. For example, this may be any of ``{db1, db2,
pywt.wavelist outputs. For example, this may be any of ``{db1, db2,
db3, db4, haar}``.
sigma : float, optional
The standard deviation of the noise. The noise is estimated when sigma
is None (the default).
is None (the default) by the method in [2]_.
threshold : float, optional
The thresholding value. All wavelet coefficients less than this value
are set to 0. The default value (None) uses the SureShrink method found
@@ -369,13 +369,16 @@ def _wavelet_threshold(img, wavelet, threshold=None, sigma=None, mode='soft'):
thresholding for image denoising and compression." Image Processing,
IEEE Transactions on 9.9 (2000): 1532-1546.
DOI: 10.1109/83.862633
.. [2] D. L. Donoho and I. M. Johnstone. "Ideal spatial adaptation
by wavelet shrinkage." Biometrika 81.3 (1994): 425-455.
DOI: 10.1093/biomet/81.3.425
"""
import pywt
coeffs = pywt.wavedecn(img, wavelet=wavelet)
detail_coeffs = coeffs[-1]['d' * img.ndim]
if sigma is None:
# Estimate the noise std.dev as discussed in PR #1837
# Estimates via the noise via method in [2]
sigma = np.median(np.abs(detail_coeffs)) / 0.67448975019608171
if threshold is None:
@@ -398,7 +401,8 @@ def denoise_wavelet(img, sigma=None, wavelet='db1', mode='soft'):
of the denoised image.
sigma : float, optional
The noise standard deviation used when computing the threshold
adaptively as described in [1]_.
adaptively as described in [1]_. When None (default), the noise
standard deviation is estimated via the method in [2]_.
wavelet : string, optional
The type of wavelet to perform and can be any of the options
[pywt.wavelist]_ outputs. The default is `'db1'`. For example,
@@ -437,6 +441,9 @@ def denoise_wavelet(img, sigma=None, wavelet='db1', mode='soft'):
thresholding for image denoising and compression." Image Processing,
IEEE Transactions on 9.9 (2000): 1532-1546.
DOI: 10.1109/83.862633
.. [2] D. L. Donoho and I. M. Johnstone. "Ideal spatial adaptation
by wavelet shrinkage." Biometrika 81.3 (1994): 425-455.
DOI: 10.1093/biomet/81.3.425
Examples
--------
skimage/restoration/tests/test_denoise.py
+11 -11
View File
@@ -309,19 +309,19 @@ def test_no_denoising_for_small_h():
def test_wavelet_denoising():
img = astro_gray.copy() + 0.1 * np.random.randn(*(astro_gray.shape))
img = np.clip(img, 0, 1)
for img in [astro_gray, astro]:
img = img.copy() + 0.1 * np.random.randn(*(img.shape))
img = np.clip(img, 0, 1)
# less energy in signal
assert restoration.denoise_wavelet(img).sum() <= img.sum()
# less energy in signal
assert restoration.denoise_wavelet(img).sum() <= img.sum()
# test changing noise_std (higher threshold, so less energy in signal)
assert (restoration.denoise_wavelet(img, noise_stdev=0.2).sum() <=
restoration.denoise_wavelet(img, noise_stdev=0.1).sum())
# test changing noise_std (higher threshold, so less energy in signal)
assert (restoration.denoise_wavelet(img, noise_stdev=0.2).sum() <=
restoration.denoise_wavelet(img, noise_stdev=0.1).sum())
# This works for this particular image (probably more wavelet theory here)
assert (np.sum(restoration.denoise_wavelet(img, wavelet='db2')) <
np.sum(restoration.denoise_wavelet(img, wavelet='db1')))
# This works for this particular image
assert (np.sum(restoration.denoise_wavelet(img, wavelet='db2')) <
np.sum(restoration.denoise_wavelet(img, wavelet='db1')))
if __name__ == "__main__":