estimate_sigma(image, average_sigmas=False, multichannel=False)
This function assumes the noise follows a Gaussian distribution. The estimation algorithm is based on the median absolute deviation of the wavelet detail coefficients as described in section 4.2 of .
Image for which to estimate the noise standard deviation.
If true, average the channel estimates of :None:None:`sigma`. Otherwise return a list of sigmas corresponding to each channel.
Estimate sigma separately for each channel.
Estimated noise standard deviation(s). If :None:None:`multichannel` is True and :None:None:`average_sigmas` is False, a separate noise estimate for each channel is returned. Otherwise, the average of the individual channel estimates is returned.
Robust wavelet-based estimator of the (Gaussian) noise standard deviation.
>>> import skimage.dataSee :
... from skimage import img_as_float
... img = img_as_float(skimage.data.camera())
... sigma = 0.1
... img = img + sigma * np.random.standard_normal(img.shape)
... sigma_hat = estimate_sigma(img, multichannel=False)
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.restoration._denoise.estimate_sigma
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