sum_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10)

This is an edge-preserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity.

Spatial closeness is measured by considering only the local pixel neighborhood given by a structuring element (selem).

Radiometric similarity is defined by the greylevel interval [g-s0, g+s1] where g is the current pixel greylevel.

Only pixels belonging to the structuring element AND having a greylevel inside this interval are summed.

Note that the sum may overflow depending on the data type of the input array.

Parameters

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1's and 0's.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

s0, s1 : int

Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value.

Returns

out : 2-D array (same dtype as input image)

Output image.

Apply a flat kernel bilateral filter.

See Also

denoise_bilateral

Examples

This example is valid syntax, but we were not able to check execution

>>> from skimage import data
... from skimage.morphology import disk
... from skimage.filters.rank import sum_bilateral
... img = data.camera().astype(np.uint16)
... bilat_img = sum_bilateral(img, disk(10), s0=10, s1=10)

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

skimage.filters.rank.bilateral.sum_bilateral

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