`skimage.filters.rank.generic:subtract_mean`

source: /dev/scikit-image/src/skimage/filters/rank/generic.py :834

Signature

   def     subtract_mean (    image    ,    footprint    ,    out    =  None   ,    mask    =  None   ,    shift_x    =  0   ,    shift_y    =  0   ,    shift_z    =  0     )

Summary

Return image subtracted from its local mean.

Parameters

image : ndarray of shape ([P,] M, N) and dtype (uint8 or uint16): Input image.
footprint : ndarray: The neighborhood expressed as an ndarray of 1's and 0's.
out : ndarray of shape ([P,] M, N), same dtype as input `image`: If None, a new array is allocated.
mask : ndarray of dtype (int or float), optional: 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, shift_z : int: Offset added to the footprint center point. Shift is bounded to the footprint sizes (center must be inside the given footprint).

Returns

out : ([P,] M, N) ndarray, same dtype as `image`: Output image.

Notes

Subtracting the mean value may introduce underflow. To compensate this potential underflow, the obtained difference is downscaled by a factor of 2 and shifted by n_bins / 2 - 1, the median value of the local histogram (n_bins = max(3, image.max()) +1 for 16-bits images and 256 otherwise).

Examples

from skimage import data
from skimage.morphology import disk, ball
from skimage.filters.rank import subtract_mean
import numpy as np
img = data.camera()
rng = np.random.default_rng()
volume = rng.integers(0, 255, size=(10,10,10), dtype=np.uint8)
out = subtract_mean(img, disk(5))
out_vol = subtract_mean(volume, ball(5))

✓

Aliases

skimage.filters.rank.subtract_mean