Generating training sequences of noises and finding kurtosis and skewness of them

Hi everyone,

I am trying to generate training sequences of different noises but I am not confirmed that whether am doing it in a correct way or not.

Here is the code snippet for generating them,

%% for uniform image

% image = uint8(ones([512,512])*128);

A = -30;

B = 30;

matrix_uniform = uint8(A + (B-A)*rand(size(image)));

%for gaussian image

Mean_added = 0;

Variance_added=400;

a = ones(512)*128;

matrix_gaussian = uint8(a + Mean_added+sqrt(Variance_added).*randn(size(a)));

%% for impulse noise

image = ones(512)*128;

matrix_impulse = imnoise(uint8(image),'salt & pepper',.4);

%% for speckle noise

image = ones(512)*128;

matrix_speckle = imnoise(uint8(image),'speckle',.1);

Am I doing it in correct way.

Also how to find their kurtosis and skewness using Matlab, actually I want to get reference kurtosis and skewness to compare with the skewness and kurtosis of noise extracted from the image to check the performance of algorithm.

Answer 1

[Image Analyst]

See my image moments demo, where I compute skewness and kurtosis.

(I know this is old, but perhaps someone else will like the demo.)