Guided filtering of images
filters the image B
= imguidedfilter(___,Name,Value
)A
using name-value pairs to
control aspects of guided filtering.
The DegreeOfSmoothing
argument specifies
a soft threshold on variance for the given neighborhood. If
a pixel's neighborhood has variance much lower than the
threshold, it will see some amount of smoothing. If a
pixel's neighborhood has variance much higher than the
threshold it will have little to no smoothing.
Input images A
and G
can be of different classes. If either
A
or G
is
of class integer or logical, then
imguidedfilter
converts them to
floating-point precision for internal computation.
Input images A
and G
can
have different number of channels.
If both A
and
G
are RGB images, then
imguidedfilter
filters each
channel of A
independently
using the corresponding channel of
G
.
If A
is an RGB image and
G
is a single-channel image,
then imguidedfilter
filters
each channel of A
independently using the same guidance image,
G
.
If A
is a single-channel
image and G
is an RGB image,
then imguidedfilter
filters
A
using the combined color
statistics of all the three channels of
G
.
[1] Kaiming He, Jian Sun, Xiaoou Tang. Guided Image Filtering. IEEE® Transactions on Pattern Analysis and Machine Intelligence, Volume 35, Issue 6, pp. 1397-1409, June 2013.