% Coherent Noise Detection
function [hist_image, EDGES, final_EDGES] = coherent_noise(image,std_mult)
% This routine defines blank, noisy images that have hist_images that are not
% single-moded (e.g. like Gaussian or Poisson noise) as having coherent
% noise, i.e. artifacts in the image that are not random and not necessarily repetitive.
% This is useful for determing if an imaging sensor is working properly
% by imaging a uniform scene/target and confirming that the only signal
% variations are due to sensor noise.
% The routine returns the locations of the modes in the hist_image
% Variable "ignore" is the minimum mode height to be recognized.
% Any frequency of occurrence smaller than this value is considered noise.
[hist_image,EDGES,BIN] = histcounts(image);
figure(100)
plot(EDGES(2:end),hist_image)
length_of_hist_image=length(hist_image);
for i=1:length_of_hist_image-2,
coherent(i)=0;
A=hist_image(i+2)-hist_image(i+1);
B=hist_image(i+1)-hist_image(i);
% If the hist_image indicates a fall from a peak designate this point as a mode
if ((A<0) & (B>=0)), coherent(i)=1;
end;
end;
[mode]=find(coherent);
% Make sure that local peaks are not simply noise and that modes are separated by a minimum distance in terms of multiples of the image standard deviation.
std_image=std2(image);
min_separation=std_mult*std_image;
EDGES(mode(1))=EDGES(mode(2));
bin_index=0;
for bin=2:length(mode),
if(EDGES(mode(bin))-EDGES(mode(bin-1)))>min_separation,
bin_index=bin_index+1;
final_mode(bin_index)=bin_index;
final_EDGES(bin_index)=EDGES(mode(bin));
end
end
