bundles / skimage latest / skimage / filters / rank / _percentile
module
skimage.filters.rank._percentile
source: /dev/scikit-image/src/skimage/filters/rank/_percentile.py :0
Members
-
_apply -
autolevel_percentile -
enhance_contrast_percentile -
gradient_percentile -
mean_percentile -
percentile -
pop_percentile -
subtract_mean_percentile -
sum_percentile -
threshold_percentile
Summary
Inferior and superior ranks, provided by the user, are passed to the kernel function to provide a softer version of the rank filters. E.g. autolevel_percentile will stretch image levels between percentile [p0, p1] instead of using [min, max]. It means that isolated bright or dark pixels will not produce halos.
Extended Summary
The local histogram is computed using a sliding window similar to the method described in [1].
Input image can be 8-bit or 16-bit, for 16-bit input images, the number of histogram bins is determined from the maximum value present in the image.
Result image is 8-/16-bit or double with respect to the input image and the rank filter operation.
Additional content
Inferior and superior ranks, provided by the user, are passed to the kernel function to provide a softer version of the rank filters. E.g. autolevel_percentile will stretch image levels between percentile [p0, p1] instead of using [min, max]. It means that isolated bright or dark pixels will not produce halos.
The local histogram is computed using a sliding window similar to the method described in [1].
Input image can be 8-bit or 16-bit, for 16-bit input images, the number of histogram bins is determined from the maximum value present in the image.
Result image is 8-/16-bit or double with respect to the input image and the rank filter operation.
References
Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18.
Aliases
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skimage.filters.rank._percentile