Simulate a sensor that combines data from multiple pixel sizes.

The simulation captures image data with a series of sensors, each has a different pixel size. Because all the simulated sensors have the same dye size, it is possible to figure out which row/col values correspond in the different sensors (not shown here).

An HDR algorithm might extract data data from corresponding positions in the different simulated sensors and integrate these values into a single image.

See also: sceneFromFile, sensorCompute, ipWindow, ipCompute

Read a high dynamic range scene
Create an optical image with the default lens parameters (f# = 2.0)
A series of sensors with different pixel sizes but same dye size
Run the main loop
Look at the series of images that were created.
Show the different resolution images side by side
END

ieInit

Read a high dynamic range scene

fName = fullfile(isetRootPath,'data','images','multispectral','Feng_Office-hdrs.mat');

% Read in a multispectral file with high dynamic range and a mean level of
% 200 cd/m2
scene = sceneFromFile(fName,'multispectral',200);

% ieAddObject(scene); sceneWindow

Reading multispectral data with mcCOEF.
Saved using svd method

Create an optical image with the default lens parameters (f# = 2.0)

oi = oiCreate;
oi = oiCompute(oi,scene);
% ieAddObject(oi); oiWindow

A series of sensors with different pixel sizes but same dye size

clear psSize;
pSize = [1 2 4];        % Microns
dyeSizeMicrons = 512;   % Microns
fillFactor = 0.5;       % Percentage of pixel containing photodector

% We will have a cell array of sensors
sensor = cell(length(pSize),1);

% The sensor will be the same as the base sensor, but we will adjust their
% pixel sizes (keeping dye size constant)
baseSensor = sensorCreate('monochrome');             % Initialize
baseSensor = sensorSet(baseSensor,'expTime',0.003);  % 3 ms exposure time

% This is the base processor image. We store the rendered image here
baseProcessor = ipCreate;
ip = cell(length(pSize),1);

Run the main loop

% We simulate a series of monochrome sensors with different pixel sizes We
% then render the images and place them in the virtual camera image window.
% We can leaf through them and see the effects of scaling the pixel sizes.
for ii=1:length(pSize)

    % Adjust the pixel size (meters), constant fill
    sensor{ii} = sensorSet(baseSensor,'pixel size constant fill factor',[pSize(ii) pSize(ii)]*1e-6);

    %Adjust the sensor row and column size so that the sensor has a constant
    %field of view.
    sensor{ii} = sensorSet(sensor{ii},'rows',round(dyeSizeMicrons/pSize(ii)));
    sensor{ii} = sensorSet(sensor{ii},'cols',round(dyeSizeMicrons/pSize(ii)));

    sensor{ii} = sensorCompute(sensor{ii},oi);
    sensor{ii} = sensorSet(sensor{ii},'name',sprintf('pSize %.1f',pSize(ii)));
    ieAddObject(sensor{ii});

    ip{ii} = ipCompute(baseProcessor,sensor{ii});
    ip{ii} = ipSet(ip{ii},'name',sprintf('pSize %.1f',pSize(ii)));

    ieAddObject(ip{ii});
end

Look at the series of images that were created.

% The images have different spatial resolutions and row/col sizes. This
% brings up the window.  Click around. We suggest setting the display gamma
% (text box, lower left of the window) to about 0.6
ipWindow;

% You can extract the raw data from different sources. To read out the
% voltages from the sensor directly you can use
ii = 2;
v = sensorGet(sensor{ii},'volts');
size(v)
% The size of v is equal to the number of pixels on the sensor

% To read out the rgb data from the virtual camera image (vci) structures
% you can use
ii = 1;
dv = ipGet(ip{ii},'result');
size(dv)
% The dv are RGB images, but since they are acquired with a monochrome
% camera the RGB values are equal.

ans =

   256   256


ans =

   512   512     3

Show the different resolution images side by side

imageMultiview('ip',1:3,true);