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Latest analysis routine.

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Karthik 2025-02-28 17:30:35 +01:00
parent 21716395c5
commit fbd808fc8b
1 changed files with 116 additions and 20 deletions
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136
Imaging-Response-Function-Extractor/extractIRF.m
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@ -193,12 +193,12 @@ for k = 1 : length(od_imgs)
drawnow;
end
%% Compute the average 2D spectrum and do radial averaging to get the 1D spectrum
%% Compute the average 2D spectrum
% Compute the average power spectrum.
averagePowerSpectrum = mean(cat(3, density_noise_spectrum{:}), 3, 'double');
% Plot the average power spectrum.
%% Plot the average power spectrum and the 1-D Radial Imaging Response Function
figure(2)
clf
set(gcf,'Position',[100, 100, 1500, 700])
@ -272,7 +272,7 @@ initialGuess = [2E6, 1E-6, 1E-6, 1E-6, 1E-6, 0.8];
% Set upper and lower bounds for the parameters (optional)
lb = [-Inf, -Inf, -Inf, -Inf, -Inf, 0]; % Lower bounds
ub = [Inf, Inf, Inf, Inf, Inf, 1]; % Upper bounds
ub = [Inf, Inf, Inf, Inf, Inf, 1]; % Upper bounds
% Perform the non-linear least squares fitting using lsqcurvefit
options = optimoptions('lsqcurvefit', 'Display', 'iter'); % Display iterations during fitting
@ -285,14 +285,87 @@ fittedProfile = RadialImagingResponseFunction(C_fit, k_new, kmax);
nexttile(2)
plot(kvec, NormalisedProfile, 'o-', 'MarkerSize', 4, 'MarkerFaceColor', 'none', 'DisplayName', 'Radial (average) profile');
hold on
plot(k_new, fittedProfile, 'r-', 'DisplayName', 'Fitted Curve');
plot(k_new, fittedProfile, 'r-', 'DisplayName', 'Fit');
set(gca, 'XScale', 'log'); % Setting x-axis to log scale
xlabel('k_\rho (\mum^{-1})', 'Interpreter', 'tex', 'FontSize', 16)
ylabel('Normalised amplitude', 'FontSize', 16)
title('Modulation Transfer Function', 'FontSize', 16);
title('Imaging Response Function', 'FontSize', 16);
legend('FontSize', 16);
grid on;
%% 2-D Imaging Response Function fit
% Get the size of the matrix
[nRows, nCols] = size(averagePowerSpectrum);
% Compute the center index
centerRow = round(nRows / 2);
centerCol = round(nCols / 2);
% Remove the central DC component by setting it to zero
averagePowerSpectrum(centerRow, centerCol) = 0;
% Define the objective function for fitting
function residuals = fitImagingResponse(params, averagePowerSpectrum)
% Calculate the ImagingResponseFunction with the given parameters
M = ImagingResponseFunction(params);
% Compute the residuals as the difference between the matrices
residuals = M(:) - averagePowerSpectrum(:); % Flatten both matrices
end
% Initial guess for parameters (from your example)
initialParams = [0.65, 0.1, 0.1, 0.1, 0.1, 0.1, 1E-6, 1E-8, 80];
% Set options for the optimizer (optional, helps with debugging)
options = optimoptions('lsqnonlin', 'Display', 'iter', 'MaxFunctionEvaluations', 5000);
% Perform the fitting using lsqnonlin
optimalParams = lsqnonlin(@(params) fitImagingResponse(params, averagePowerSpectrum), initialParams, [], [], options);
AstigmatismCoefficient = optimalParams(2);
SphericalAberrationCoefficient = optimalParams(3);
DefocussingCoefficient = optimalParams(5);
% Compute the best-fit M using the optimized parameters
bestFitM = ImagingResponseFunction(optimalParams);
residuals = fitImagingResponse(optimalParams, averagePowerSpectrum);
% Plot the fitted result
figure(3)
clf
set(gcf,'Position',[100, 100, 1500, 500])
% Create tiled layout with 2 rows and 3 columns
t = tiledlayout(1, 3, 'TileSpacing', 'compact', 'Padding', 'compact');
nexttile(1);
imagesc(abs(10*log10(averagePowerSpectrum)))
axis equal tight;
colorbar
colormap(flip(jet));
title('Average Density Noise Spectrum', 'FontSize', 16);
grid on;
nexttile(2);
imagesc(abs(10*log10(bestFitM)))
axis equal tight;
colorbar
colormap(flip(jet));
title('Fitted Imaging Response Function', 'FontSize', 16);
grid on;
nexttile(3);
resmat = reshape(residuals, size(bestFitM));
imagesc(abs(10*log10(resmat)))
axis equal tight;
colorbar
colormap(flip(jet));
title('Fit residuals', 'FontSize', 16);
grid on;
%% Helper Functions
function ret = getBkgOffsetFromCorners(img, x_fraction, y_fraction)
@ -439,25 +512,48 @@ function [optrefimages] = removefringesInImage(absimages, refimages, bgmask)
end
function [M] = ImagingResponseFunction(B)
x = -100:100;
y = x;
x = -128:127;
y = x;
[X,Y] = meshgrid(x,y);
R = sqrt(X.^2+Y.^2);
PHI = atan2(X,Y)+pi;
%fit parameters
tau = B(1);
R = sqrt(X.^2+Y.^2);
PHI = atan2(X,Y)+pi;
% Fit parameters
tau = B(1);
alpha = B(2);
S0 = B(3);
phi = B(4);
beta = B(5);
S0 = B(3);
phi = B(4);
beta = B(5);
delta = B(6);
A = B(7);
C = B(8);
a = B(9);
U = heaviside(1-R/a).*exp(-R.^2/a^2/tau^2);
A = B(7);
C = B(8);
a = B(9);
U = heaviside(1-R/a).*exp(-R.^2/a^2/tau^2);
THETA = S0*(R/a).^4 + alpha*(R/a).^2.*cos(2*PHI-2*phi) + beta*(R/a).^2;
p = U.*exp(1i.*THETA);
M = A*abs((ifft2(real(exp(1i*delta).*fftshift(fft2(p)))))).^2 + C;
p = U.*exp(1i.*THETA);
M = A*abs((ifft2(real(exp(1i*delta).*fftshift(fft2(p)))))).^2 + C;
end
function [p] = exitPupilFunction(B)
x = -128:127;
y = x;
[X,Y] = meshgrid(x,y);
R = sqrt(X.^2+Y.^2);
PHI = atan2(X,Y)+pi;
% Fit parameters
tau = B(1);
alpha = B(2);
S0 = B(3);
phi = B(4);
beta = B(5);
delta = B(6);
A = B(7);
C = B(8);
a = B(9);
U = heaviside(1-R/a).*exp(-R.^2/a^2/tau^2);
THETA = S0*(R/a).^4 + alpha*(R/a).^2.*cos(2*PHI-2*phi) + beta*(R/a).^2;
p = U.*exp(1i.*THETA);
end
function [R, Zr] = getRadialProfile(data, radialStep)