Problems with the experimental approach of measuring PRNU

Photoreceptor response nonuniformity (PRNU) and dark-signal nonuniformity (DSNU) can be estimated by measuring a uniform scene at several exposure levels, taking care to avoid saturatation or values near zero. Then, one can find the slopes of the pixel responses, normalize them, and estimate the PRNU from the standard deviation of these slopes.

This script simulates that approach. It shows that the photon noise corrupts the estimate. Even when there is 0 PRNU, the PRNU appears to be on the order of 2 percent in the presence of photon noise.

Further observations

Notice that the slope of the pixel response depends on the level of the uniform field (it will be higher on a bright field). To normalize for the level of the field, specify the standard deviation of the slope as a percentage of the mean slope. You can do this by dividing all the slopes by the mean slope before calculating the standard deviation. The mean slope is always 1 volt/sec.

Copyright ImagEval Consultants, LLC, 2010.

Contents

ieInit

Parameters

% scene parameters
meanL = 100; % Mean luminance
fov   = 2;   % Field of view

% Set to 0 implicitly when simulateNoise = 0
prnuLevel =  1;     % Std. dev of gain, around 1, as a percentage

% Other sensor noises
dsnuLevel = 0.00;   % Std. dev. of offset in volts
readNoise = 0.00;   % Read noise in volts
darkNoise = 0.00;   % Dark voltage in v/s

% Set a range of experimental exposure times (in secs)
expTime = (40:2:60)/1000;      % Times in sec
nRepeats = 3;                  % We can repeat the experiment a few times

Make a uniform scene, oi, and sensor

scene = sceneCreate('uniform ee');
scene = sceneAdjustLuminance(scene,meanL);
scene = sceneSet(scene,'fov',fov);

oi = oiCreate('default',[],[],0);
optics = oiSet(oi,'optics offaxis method','skip');  % No relative illumination

sensor = sensorCreate;
sensor = sensorSet(sensor,'size',[196 196]);
sensor = sensorSet(sensor,'noise flag',2);

% Make the uniform scene larger than the sensor field of view.
scene = sceneSet(scene,'fov',sensorGet(sensor,'fov')*1.5);

% Compute the optical image
oi = oiCompute(scene,oi);
ieAddObject(oi);
oiWindow;

Set the sensor parameters

expTime = repmat(expTime,1,nRepeats);

sensor = sensorClearData(sensor);
sensor = sensorSet(sensor,'DSNU level',dsnuLevel);
sensor = sensorSet(sensor,'PRNU level',prnuLevel);
sensor = sensorSet(sensor,'pixel Read noise volts',readNoise);
sensor = sensorSet(sensor,'pixel Dark voltage',darkNoise);

% How many color filters?  Normally 3 and we use the 2nd one in a Bayer.
% But sometimes we might run this script with a monochrome.
nFilters = sensorGet(sensor,'nfilters');

Acquire multiple short exposures of the dark image

% We take the image multiple times so we can average out the read noise
nTimes = length(expTime);

% Zero out the voltages
nSamp = prod(sensorGet(sensor,'size'))/2;
volts = zeros(nSamp,nTimes);

wBar = waitbar(0,'Acquiring images');
for ii=1:nTimes
    waitbar(ii/nTimes,wBar);
    sensor = sensorSet(sensor,'Exposure Time',expTime(ii));
    sensor = sensorCompute(sensor,oi,0);
    volts(:,ii) = sensorGet(sensor,'volts',2);
end
close(wBar);

Make sure the sensor was fully illuminated

% Should be uniform and filled up!
ieAddObject(sensor); sensorWindow('scale',true);

Compute the best-fitting line for expTime vs. voltage for each pixel

% volts' = expTime * x
% x = inv(A)*volts'
A = [expTime(:), ones(nTimes,1)];
x = A\volts';

slopes  = x(1,:);
slopes = slopes/mean(slopes(:));

% This is another way to estimate DSNU.
offsets = x(2,:);

Plot the data and analyze the values.

vcNewGraphWin;
t = sprintf('Normalized slopes (photon noise)');

hist(slopes,50); title(t)
set(gca,'xlim',[0.9 1.1]);

PRNU = 100*std(slopes); % Std. of slope as a percentage (not fraction)

fprintf('---------------------------\n')
fprintf('PRNU percentage estimated is too high! %.1f\n',PRNU);
fprintf('---------------------------\n')
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PRNU percentage estimated is too high! 4.0
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