s_HumanSensor

We are leaving this script here as a historical artifact. At this point, it is best to use scripts and functions in ISETBio for creating models of the human cone mosaic.

Create a sensor that models the spatial absorptions of the human cone mosaic. The cone mosaic comprises randomly positioned L,M,S cones at some density (typically 4:2:1) and also some blank positions (K).

The parameters for the cone optical and noise properties can be set as part of the sensor structure.

Initialize ISET
Create a test scene and optical image
Create the human sensor with a size matched to the scene
Adjust pixel properties for human - We need a rationale for values.
Show the cone absorption counts across a horizontal image line
To retrieve and plot the L,M and S cone absorptions you can use this:
Create a dichromatic sensor mosaic
END

if isempty(which('Lens'))
    fprintf('%s requires ISETBio (Lens).',mfilename);
    return;
end

s_humanSensor requires ISETBio (Lens).

Initialize ISET

ieInit

Create a test scene and optical image

We illustrate a sweep frequency scene that spans 1 deg field of view. We pass that scene through default human optics.

scene = sceneCreate('sweep');
hFov = 1;   % Horizontal field of view (deg)
scene = sceneSet(scene,'fov',hFov);
% sceneWindow(scene);

oi = oiCreate('human');
oi = oiCompute(oi,scene);
% oiWindow(oi);

Create the human sensor with a size matched to the scene

The code here illustrates the complete set of parameters to create a sensor mosaic that simulates the cone absorptions. The cone aperture is roughly the size in the fovea, though perhaps a bit small. The spacing between cones is a little big. These are among the unknown parameters across the retinal surface.

coneSpacing = 1.5;                                % um;
coneAperture = [coneSpacing coneSpacing]*1e-6;    % meters

% This is a pretty good number to summarize human optics with a 60 diopter
% (17 mm) focal length.
micronPerDegree = 300;
degPerCone = coneAperture(1)*1e6/micronPerDegree;

% nCones = fov/degPerCone, and we make it a bit larger
nConesHRealSize = floor((hFov/degPerCone));
vFov = sceneGet(scene,'vfov');
nConesVRealSize = floor(vFov/degPerCone);

% Specify the relative densities of empty (K) and L,M,S cone ratios
rgbDensities = [1, 8, 4, 2];
rgbDensities = rgbDensities/sum(rgbDensities);

sz = [nConesVRealSize nConesHRealSize];
fprintf('Creating  %.0fx%.0f cone mosaic\n',nConesVRealSize,nConesHRealSize);

rSeed = 10;  % So we can always repeat this exactly
% An alternative call is
params.sz = sz;
params.rgbDensities = rgbDensities;
params.coneAperture = coneAperture;
params.rSeed = rSeed;
sensor = sensorCreate('human',[],params);

% Alternative way to make this call is:
%
% [sensor, xy, coneType, rSeed] = ...
%     sensorCreateConeMosaic(sensorCreate, sz,rgbDensities,coneAperture,rSeed);

% View the cone mosaic
sensorConePlot(sensor);
title('Cone mosaic');

% Check that the FOVs are about equal for the scene and the sensor.
fprintf('Sensor fov: %f\n',sensorGet(sensor,'fov',scene,oi));
fprintf('Scene fov: %f\n',sceneGet(scene,'fov'));

Adjust pixel properties for human - We need a rationale for values.

Writing these kinds of programs makes it clear that we need better
parameters for these aspects of the cone properties.  Fred Rieke, to the
rescue, one hopes.

voltageSwing = 0.2; %

pixel = sensorGet(sensor,'pixel');
pixel = pixelSet(pixel,'darkVoltage',0);
pixel = pixelSet(pixel,'readNoiseVolts',0.0005);
pixel = pixelSet(pixel,'voltageSwing',0.02);

sensor = sensorSet(sensor,'pixel',pixel);
sensor = sensorCompute(sensor,oi);
sensorWindow(sensor);

Show the cone absorption counts across a horizontal image line

The absorptions in the L,M, and S are shown as red, green and blue. The black curve at the top is a measure of the typical noise we have modeled in the pixel and sensor parameters. Notice the very low contrast in the S-cones, due to chromatic aberration.

f = sensorPlotLine(sensor,[],'electrons','space',[1 116]);

To retrieve and plot the L,M and S cone absorptions you can use this:

The absorption histograms are variable in part because of photon noise,
but in this implementation there is also some dark current and read
noise that we put into the photon noise.  It is possible to set the
properties of the sensor to be ideal, so that there is only photon
noise.

ieNewGraphWin;
fColor = {'red','green','blue'};
for ii=1:3
    cData = sensorGet(sensor,'electrons',ii+1);
    histogram(cData,30,'FaceColor',fColor{ii});
    hold on
end
hold off

Create a dichromatic sensor mosaic

One can experiment with all kinds of different mosaic properties to
understand the implications of various densities, sensitivies, and noise
characteristics.
Here, I illustrate how to create a dichromatica retina. The dichromacy
is created because the L cone slot is 0 in rgbDensities. We could do
experiments trying to understand the consequences for spatial resolution
of missing a cone class.

params.sz =  [200 200];  % A little smaller
rgbDensities = [0 0 2 1];
rgbDensities = rgbDensities/sum(rgbDensities);
params.rgbDensities = rgbDensities;
sensor = sensorCreate('human',[],params);

sensorConePlot(sensor); title('Protan cone mosaic');