`skimage.restoration._denoise:estimate_sigma`

source: /dev/scikit-image/src/skimage/restoration/_denoise.py :1048

Signature

def estimate_sigma ( image , average_sigmas = False , * , channel_axis = None )

Summary

Robust wavelet-based estimator of the (Gaussian) noise standard deviation.

Parameters

image : ndarray: Image for which to estimate the noise standard deviation.
average_sigmas : bool, optional: If true, average the channel estimates of sigma. Otherwise return a list of sigmas corresponding to each channel.
channel_axis : int or None, optional: If None, the image is assumed to be grayscale (single-channel). Otherwise, this parameter indicates which axis of the array corresponds to channels.
versionadded 0.19
channel_axis was added in 0.19.

Returns

sigma : float or list: Estimated noise standard deviation(s). If multichannel is True and average_sigmas is False, a separate noise estimate for each channel is returned. Otherwise, the average of the individual channel estimates is returned.

Notes

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 ^[1].

Examples

import skimage.data
from skimage import img_as_float
img = img_as_float(skimage.data.camera())
sigma = 0.1
rng = np.random.default_rng()
img = img + sigma * rng.standard_normal(img.shape)

✓

sigma_hat = estimate_sigma(img, channel_axis=None)

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Aliases

skimage.restoration.estimate_sigma