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MAINT: more memory efficient multichannel implementation

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also make multichannel default to False to match other denoising routines
This commit is contained in:
Gregory R. Lee
2016-08-10 18:33:02 -04:00
parent c83fe7d178
commit d4168b665e
1 changed files with 6 additions and 4 deletions
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skimage/restoration/_denoise.py
+6 -4
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@@ -394,7 +394,8 @@ def _wavelet_threshold(img, wavelet, threshold=None, sigma=None, mode='soft'):
return pywt.waverecn(denoised_coeffs, wavelet)
def denoise_wavelet(img, multichannel, sigma=None, wavelet='db1', mode='soft'):
def denoise_wavelet(img, sigma=None, wavelet='db1', mode='soft',
multichannel=False):
"""Performs wavelet denoising on an image.
Parameters
@@ -458,9 +459,10 @@ def denoise_wavelet(img, multichannel, sigma=None, wavelet='db1', mode='soft'):
img = img_as_float(img)
if multichannel:
out = np.stack([_wavelet_threshold(img[..., c], wavelet=wavelet,
mode=mode, sigma=sigma)
for c in range(img.ndim)], axis=-1)
out = np.empty_like(img)
for c in range(img.ndim):
out[..., c] = _wavelet_threshold(img[..., c], wavelet=wavelet,
mode=mode, sigma=sigma)
else:
out = _wavelet_threshold(img, wavelet=wavelet, mode=mode,
sigma=sigma)