sp_distribution🔗
Short description🔗
sp_distribution — Generate a salt-and-pepper noise distribution.
Signature🔗
sp_distribution( number PercentSalt, number PercentPepper, out distribution.values Distribution )
Description🔗
sp_distribution generates a noise distribution with the
values 0 and 255. The parameters PercentSalt and
PercentPepper determine the percentage of white and black
noise pixels, respectively. The sum of these parameters must be
smaller than 100. Usually, the result Distribution is
used as input for the operator add_noise_distribution.
Execution information🔗
Execution information
-
Multithreading type: reentrant (runs in parallel with non-exclusive operators).
-
Multithreading scope: global (may be called from any thread).
-
Processed without parallelization.
Parameters🔗
PercentSalt (input_control) number → (real / integer)
Percentage of salt (white noise pixels).
Default: 5.0
Suggested values: 1.0, 2.0, 5.0, 7.0, 10.0, 15.0, 20.0, 30.0
Value range: 0.0 ≤ PercentSalt ≤ 100.0
Minimum increment: 0.1
Recommended increment: 1.0
Restriction: (PercentSalt + PercentPepper) <= 100
PercentPepper (input_control) number → (real / integer)
Percentage of pepper (black noise pixels).
Default: 5.0
Suggested values: 1.0, 2.0, 5.0, 7.0, 10.0, 15.0, 20.0, 30.0
Value range: 0.0 ≤ PercentPepper ≤ 100.0
Minimum increment: 0.1
Recommended increment: 1.0
Restriction: (PercentSalt + PercentPepper) <= 100
Distribution (output_control) distribution.values-array → (real)
Resulting noise distribution.
Number of elements: 513
Example🔗
(HDevelop)
read_image(Image,'fabrik')
dev_display (Image)
sp_distribution(30,30,Dist)
add_noise_distribution(Image,ImageNoise,Dist)
dev_display (ImageNoise)
read_image(&Image,"fabrik")\;
disp_image(Image,WindowHandle)\;
create_tuple(&PerSalt,1)\;
set_d(PerSalt,30.0,0)\;
create_tuple(&PerPepper,1)\;
set_d(PerPepper,30.0,0)\;
T_sp_distribution(PerSalt,PerPepper,&Dist)\;
T_add_noise_distribution(Image,&ImageNoise,Dist)\;
disp_image(ImageNoise,WindowHandle)\;
Combinations with other operators🔗
Combinations
Possible successors
Alternatives
gauss_distribution, noise_distribution_mean
See also
gauss_distribution, noise_distribution_mean, add_noise_white
Module🔗
Foundation