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bundles / skimage 0.26.1rc0.dev0+git20260530.b607368ff / skimage / restoration / _denoise / denoise_bilateral

function

skimage.restoration._denoise:denoise_bilateral

source: /dev/scikit-image/src/skimage/restoration/_denoise.py :95

Signature

def   denoise_bilateral ( image ,  win_size = None ,  sigma_color = None ,  sigma_spatial = 1 ,  bins = 10000 ,  mode = constant ,  cval = 0 ,  * ,  channel_axis = None )

Summary

Denoise image using bilateral filter.

Parameters

image : ndarray, shape (M, N[, 3])

Input image, 2D grayscale or RGB.

win_size : int

Window size for filtering. If win_size is not specified, it is calculated as max(5, 2 * ceil(3 * sigma_spatial) + 1).

sigma_color : float

Standard deviation for grayvalue/color distance (radiometric similarity). A larger value results in averaging of pixels with larger radiometric differences. If None, the standard deviation of image will be used.

sigma_spatial : float

Standard deviation for range distance. A larger value results in averaging of pixels with larger spatial differences.

bins : int

Number of discrete values for Gaussian weights of color filtering. A larger value results in improved accuracy.

mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}

How to handle values outside the image borders. See numpy.pad for detail.

cval : int or float

Used in conjunction with mode 'constant', the value outside the image boundaries.

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.

Returns

denoised : ndarray

Denoised image.

Notes

This is an edge-preserving, denoising filter. It averages pixels based on their spatial closeness and radiometric similarity [1].

Spatial closeness is measured by the Gaussian function of the Euclidean distance between two pixels and a certain standard deviation (sigma_spatial).

Radiometric similarity is measured by the Gaussian function of the Euclidean distance between two color values and a certain standard deviation (sigma_color).

Note that, if the image is of any int dtype, image will be converted using the img_as_float function and thus the standard deviation (sigma_color) will be in range [0, 1].

For more information on scikit-image's data type conversions and how images are rescaled in these conversions, see: https://scikit-image.org/docs/stable/user_guide/data_types.html.

Examples

from skimage import data, img_as_float
astro = img_as_float(data.astronaut())
astro = astro[220:300, 220:320]
rng = np.random.default_rng()
noisy = astro + 0.6 * astro.std() * rng.random(astro.shape)
noisy = np.clip(noisy, 0, 1)
denoised = denoise_bilateral(noisy, sigma_color=0.05, sigma_spatial=15,
                             channel_axis=-1)
✓

Aliases

  • skimage.restoration.denoise_bilateral

Referenced by

This package