is it possible to write a code that experiments with zero mean Gaussian noise for a variance in the range 0.001 to 0.1

is it possible to write a code or algorithm that basically does the following

J = imnoise(MEANPIC,'gaussian',0.001); h=figure; imshow(J); set(h,'Name','0.001');

J = imnoise(MEANPIC,'gaussian',0.002); h=figure; imshow(J); set(h,'Name','0.002');

J = imnoise(MEANPIC,'gaussian',0.003); h=figure; imshow(J); set(h,'Name','0.003');

J = imnoise(MEANPIC,'gaussian',0.004); h=figure; imshow(J); set(h,'Name','0.004');

J = imnoise(MEANPIC,'gaussian',0.005); h=figure; imshow(J); set(h,'Name','0.005');

J = imnoise(MEANPIC,'gaussian',0.006); h=figure; imshow(J); set(h,'Name','0.006');

J = imnoise(MEANPIC,'gaussian',0.007); h=figure; imshow(J); set(h,'Name','0.007');

J = imnoise(MEANPIC,'gaussian',0.008); h=figure; imshow(J); set(h,'Name','0.008');

J = imnoise(MEANPIC,'gaussian',0.09); h=figure; imshow(J); set(h,'Name','0.009');

J = imnoise(MEANPIC,'gaussian',0.01); h=figure; imshow(J); set(h,'Name','0.01');

is it possible to have some kind of range instead of having to write out the same code each time for the different variances. thanks

Answer 1

[Rik]

Yes, this is what for loops exist for.

variance_list=.001:.001:0.01;
for k=1:numel(variance_list)
J = imnoise(MEANPIC,'gaussian',variance_list(k));
h=figure; imshow(J);
set(h,'Name',sprintf('%.3f',variance_list(k)));
end

[colm ferguson]

Thank you!