467 lines
17 KiB
Matlab
467 lines
17 KiB
Matlab
%% Parameters
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groupList = ["/images/MOT_3D_Camera/in_situ_absorption", "/images/ODT_1_Axis_Camera/in_situ_absorption", ...
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"/images/ODT_2_Axis_Camera/in_situ_absorption", "/images/Horizontal_Axis_Camera/in_situ_absorption", ...
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"/images/Vertical_Axis_Camera/in_situ_absorption"];
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folderPath = "E:/Data - Experiment/2025/05/22/";
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run = '0078';
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folderPath = strcat(folderPath, run);
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cam = 5;
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angle = 0;
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center = [1375, 2020];
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span = [200, 200];
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fraction = [0.1, 0.1];
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pixel_size = 5.86e-6;
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removeFringes = false;
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scan_parameter = 'rot_mag_fin_pol_angle';
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% scan_parameter = 'rot_mag_field';
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scan_parameter_text = 'Angle = ';
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% scan_parameter_text = 'BField = ';
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font = 'Bahnschrift';
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skipPreprocessing = true;
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skipMasking = true;
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skipIntensityThresholding = true;
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skipBinarization = true;
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%% Compute OD image, rotate and extract ROI for analysis
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% Get a list of all files in the folder with the desired file name pattern.
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filePattern = fullfile(folderPath, '*.h5');
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files = dir(filePattern);
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refimages = zeros(span(1) + 1, span(2) + 1, length(files));
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absimages = zeros(span(1) + 1, span(2) + 1, length(files));
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for k = 1 : length(files)
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baseFileName = files(k).name;
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fullFileName = fullfile(files(k).folder, baseFileName);
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fprintf(1, 'Now reading %s\n', fullFileName);
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atm_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/atoms")), angle));
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bkg_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/background")), angle));
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dark_img = double(imrotate(h5read(fullFileName, append(groupList(cam), "/dark")), angle));
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refimages(:,:,k) = subtractBackgroundOffset(cropODImage(bkg_img, center, span), fraction)';
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absimages(:,:,k) = subtractBackgroundOffset(cropODImage(calculateODImage(atm_img, bkg_img, dark_img), center, span), fraction)';
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end
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% Fringe removal
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if removeFringes
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optrefimages = removefringesInImage(absimages, refimages);
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absimages_fringe_removed = absimages(:, :, :) - optrefimages(:, :, :);
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nimgs = size(absimages_fringe_removed,3);
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od_imgs = cell(1, nimgs);
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for i = 1:nimgs
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od_imgs{i} = absimages_fringe_removed(:, :, i);
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end
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else
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nimgs = size(absimages(:, :, :),3);
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od_imgs = cell(1, nimgs);
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for i = 1:nimgs
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od_imgs{i} = absimages(:, :, i);
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end
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end
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%% Get rotation angles
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scan_parameter_values = zeros(1, length(files));
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% Get information about the '/globals' group
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for k = 1 : length(files)
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baseFileName = files(k).name;
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fullFileName = fullfile(files(k).folder, baseFileName);
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info = h5info(fullFileName, '/globals');
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for i = 1:length(info.Attributes)
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if strcmp(info.Attributes(i).Name, scan_parameter)
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if strcmp(scan_parameter, 'rot_mag_fin_pol_angle')
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scan_parameter_values(k) = 180 - info.Attributes(i).Value;
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else
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scan_parameter_values(k) = info.Attributes(i).Value;
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end
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end
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end
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end
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%% Extract g2 from experiment data
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fft_imgs = cell(1, nimgs);
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spectral_distribution = cell(1, nimgs);
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theta_values = cell(1, nimgs);
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N_bins = 32;
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Threshold = 75;
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Sigma = 2;
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N_shots = length(od_imgs);
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% Display the cropped image
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for k = 1:N_shots
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IMG = od_imgs{k};
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[IMGFFT, IMGPR] = computeFourierTransform(IMG, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization);
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% Calculate the x and y limits for the cropped image
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y_min = center(1) - span(2) / 2;
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y_max = center(1) + span(2) / 2;
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x_min = center(2) - span(1) / 2;
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x_max = center(2) + span(1) / 2;
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% Generate x and y arrays representing the original coordinates for each pixel
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x_range = linspace(x_min, x_max, span(1));
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y_range = linspace(y_min, y_max, span(2));
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[rows, cols] = size(IMGFFT);
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zoom_size = 50; % Zoomed-in region around center
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mid_x = floor(cols/2);
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mid_y = floor(rows/2);
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fft_imgs{k} = IMGFFT(mid_y-zoom_size:mid_y+zoom_size, mid_x-zoom_size:mid_x+zoom_size);
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[theta_vals, S_theta] = computeNormalizedAngularSpectralDistribution(fft_imgs{k}, 10, 20, N_bins, Threshold, Sigma);
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spectral_distribution{k} = S_theta;
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theta_values{k} = theta_vals;
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end
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% Create matrix of shape (N_shots x N_bins)
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delta_nkr_all = zeros(N_shots, N_bins);
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for k = 1:N_shots
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delta_nkr_all(k, :) = spectral_distribution{k};
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end
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% Grouping by scan parameter value (e.g., alpha)
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[unique_scan_parameter_values, ~, idx] = unique(scan_parameter_values);
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% Number of unique alpha values
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N_alpha = length(unique_scan_parameter_values);
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% Preallocate result arrays
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g2_all = zeros(N_alpha, N_bins);
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g2_error_all = zeros(N_alpha, N_bins);
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for i = 1:N_alpha
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group_idx = find(idx == i); % Indices of 20 shots for this alpha
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group_data = delta_nkr_all(group_idx, :); % (20 x N_bins) array
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for dtheta = 0:N_bins-1
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temp = zeros(length(group_idx), 1);
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for j = 1:length(group_idx)
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profile = group_data(j, :);
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profile_shifted = circshift(profile, -dtheta, 2);
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num = mean(profile .* profile_shifted);
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denom = mean(profile)^2;
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temp(j) = num / denom - 1;
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end
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g2_all(i, dtheta+1) = mean(temp);
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g2_error_all(i, dtheta+1) = std(temp) / sqrt(length(group_idx)); % Standard error
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end
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end
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% Reconstruct theta axis from any one of the stored values
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theta_vals = theta_values{1}; % assuming it's in radians
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% Number of unique alpha values
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nAlpha = size(g2_all, 1);
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% Generate a colormap with enough unique colors
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cmap = sky(nAlpha); % You can also try 'jet', 'turbo', 'hot', etc.
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figure(1);
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clf;
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set(gcf,'Position',[100 100 950 750])
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hold on;
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legend_entries = cell(nAlpha, 1);
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for i = 1:nAlpha
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errorbar(theta_vals/pi, g2_all(i, :), g2_error_all(i, :), ...
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'o-', 'Color', cmap(i,:), 'LineWidth', 1.2, ...
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'MarkerSize', 5, 'CapSize', 3);
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legend_entries{i} = sprintf('$\\alpha = %g^\\circ$', unique_scan_parameter_values(i));
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end
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ylim([-1.5 3.0]); % Set y-axis limits here
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set(gca, 'FontSize', 14);
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hXLabel = xlabel('$\delta\theta / \pi$', 'Interpreter', 'latex');
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hYLabel = ylabel('$g^{(2)}(\delta\theta)$', 'Interpreter', 'latex');
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hTitle = title('B = 2.45 G - Droplets to Stripes', 'Interpreter', 'tex');
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legend(legend_entries, 'Interpreter', 'latex', 'Location', 'bestoutside');
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set([hXLabel, hYLabel], 'FontName', font)
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set([hXLabel, hYLabel], 'FontSize', 14)
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set(hTitle, 'FontName', font, 'FontSize', 16, 'FontWeight', 'bold'); % Set font and size for title
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grid on;
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%% Helper Functions
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function [IMGFFT, IMGPR] = computeFourierTransform(I, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization)
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% computeFourierSpectrum - Computes the 2D Fourier power spectrum
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% of binarized and enhanced lattice image features, with optional central mask.
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%
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% Inputs:
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% I - Grayscale or RGB image matrix
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%
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% Output:
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% F_mag - 2D Fourier power spectrum (shifted)
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if ~skipPreprocessing
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% Preprocessing: Denoise
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filtered = imgaussfilt(I, 10);
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IMGPR = I - filtered; % adjust sigma as needed
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else
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IMGPR = I;
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end
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if ~skipMasking
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[rows, cols] = size(IMGPR);
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[X, Y] = meshgrid(1:cols, 1:rows);
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% Elliptical mask parameters
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cx = cols / 2;
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cy = rows / 2;
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% Shifted coordinates
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x = X - cx;
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y = Y - cy;
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% Ellipse semi-axes
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rx = 0.4 * cols;
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ry = 0.2 * rows;
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% Rotation angle in degrees -> radians
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theta_deg = 30; % Adjust as needed
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theta = deg2rad(theta_deg);
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% Rotated ellipse equation
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cos_t = cos(theta);
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sin_t = sin(theta);
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x_rot = (x * cos_t + y * sin_t);
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y_rot = (-x * sin_t + y * cos_t);
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ellipseMask = (x_rot.^2) / rx^2 + (y_rot.^2) / ry^2 <= 1;
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% Apply cutout mask
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IMGPR = IMGPR .* ellipseMask;
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end
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if ~skipIntensityThresholding
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% Apply global intensity threshold mask
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intensity_thresh = 0.20;
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intensity_mask = IMGPR > intensity_thresh;
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IMGPR = IMGPR .* intensity_mask;
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end
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if ~skipBinarization
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% Adaptive binarization and cleanup
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IMGPR = imbinarize(IMGPR, 'adaptive', 'Sensitivity', 0.0);
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IMGPR = imdilate(IMGPR, strel('disk', 2));
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IMGPR = imerode(IMGPR, strel('disk', 1));
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IMGPR = imfill(IMGPR, 'holes');
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F = fft2(double(IMGPR)); % Compute 2D Fourier Transform
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IMGFFT = abs(fftshift(F))'; % Shift zero frequency to center
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else
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F = fft2(double(IMGPR)); % Compute 2D Fourier Transform
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IMGFFT = abs(fftshift(F))'; % Shift zero frequency to center
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end
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end
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function [theta_vals, S_theta] = computeNormalizedAngularSpectralDistribution(IMGFFT, r_min, r_max, num_bins, threshold, sigma)
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% Apply threshold to isolate strong peaks
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IMGFFT(IMGFFT < threshold) = 0;
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% Prepare polar coordinates
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[ny, nx] = size(IMGFFT);
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[X, Y] = meshgrid(1:nx, 1:ny);
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cx = ceil(nx/2);
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cy = ceil(ny/2);
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R = sqrt((X - cx).^2 + (Y - cy).^2);
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Theta = atan2(Y - cy, X - cx); % range [-pi, pi]
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% Choose radial band
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radial_mask = (R >= r_min) & (R <= r_max);
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% Initialize the angular structure factor array
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S_theta = zeros(1, num_bins); % Pre-allocate for 180 angle bins
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% Define the angle values for the x-axis
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theta_vals = linspace(0, pi, num_bins);
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% Loop through each angle bin
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for i = 1:num_bins
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angle_start = (i-1) * pi / num_bins;
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angle_end = i * pi / num_bins;
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% Define a mask for the given angle range
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angle_mask = (Theta >= angle_start & Theta < angle_end);
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bin_mask = radial_mask & angle_mask;
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% Extract the Fourier components for the given angle
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fft_angle = IMGFFT .* bin_mask;
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% Integrate the Fourier components over the radius at the angle
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S_theta(i) = sum(sum(abs(fft_angle).^2)); % sum of squared magnitudes
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end
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% Create a 1D Gaussian kernel
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half_width = ceil(3 * sigma);
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x = -half_width:half_width;
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gauss_kernel = exp(-x.^2 / (2 * sigma^2));
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gauss_kernel = gauss_kernel / sum(gauss_kernel); % normalize
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% Apply convolution (circular padding to preserve periodicity)
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S_theta = conv([S_theta(end-half_width+1:end), S_theta, S_theta(1:half_width)], gauss_kernel, 'same');
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S_theta = S_theta(half_width+1:end-half_width); % crop back to original size
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% Normalize to 1
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S_theta = S_theta / max(S_theta);
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end
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function ret = getBkgOffsetFromCorners(img, x_fraction, y_fraction)
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% image must be a 2D numerical array
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[dim1, dim2] = size(img);
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s1 = img(1:round(dim1 * y_fraction), 1:round(dim2 * x_fraction));
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s2 = img(1:round(dim1 * y_fraction), round(dim2 - dim2 * x_fraction):dim2);
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s3 = img(round(dim1 - dim1 * y_fraction):dim1, 1:round(dim2 * x_fraction));
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s4 = img(round(dim1 - dim1 * y_fraction):dim1, round(dim2 - dim2 * x_fraction):dim2);
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ret = mean([mean(s1(:)), mean(s2(:)), mean(s3(:)), mean(s4(:))]);
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end
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function ret = subtractBackgroundOffset(img, fraction)
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% Remove the background from the image.
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% :param dataArray: The image
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% :type dataArray: xarray DataArray
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% :param x_fraction: The fraction of the pixels used in x axis
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% :type x_fraction: float
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% :param y_fraction: The fraction of the pixels used in y axis
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% :type y_fraction: float
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% :return: The image after removing background
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% :rtype: xarray DataArray
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x_fraction = fraction(1);
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y_fraction = fraction(2);
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offset = getBkgOffsetFromCorners(img, x_fraction, y_fraction);
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ret = img - offset;
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end
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function ret = cropODImage(img, center, span)
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% Crop the image according to the region of interest (ROI).
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% :param dataSet: The images
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% :type dataSet: xarray DataArray or DataSet
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% :param center: The center of region of interest (ROI)
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% :type center: tuple
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% :param span: The span of region of interest (ROI)
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% :type span: tuple
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% :return: The cropped images
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% :rtype: xarray DataArray or DataSet
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x_start = floor(center(1) - span(1) / 2);
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x_end = floor(center(1) + span(1) / 2);
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y_start = floor(center(2) - span(2) / 2);
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y_end = floor(center(2) + span(2) / 2);
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ret = img(y_start:y_end, x_start:x_end);
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end
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function ret = calculateODImage(imageAtom, imageBackground, imageDark)
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% Calculate the OD image for absorption imaging.
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% :param imageAtom: The image with atoms
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% :type imageAtom: numpy array
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% :param imageBackground: The image without atoms
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% :type imageBackground: numpy array
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% :param imageDark: The image without light
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% :type imageDark: numpy array
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% :return: The OD images
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% :rtype: numpy array
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numerator = imageBackground - imageDark;
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denominator = imageAtom - imageDark;
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numerator(numerator == 0) = 1;
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denominator(denominator == 0) = 1;
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ret = -log(double(abs(denominator ./ numerator)));
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if numel(ret) == 1
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ret = ret(1);
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end
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end
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function [optrefimages] = removefringesInImage(absimages, refimages, bgmask)
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% removefringesInImage - Fringe removal and noise reduction from absorption images.
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% Creates an optimal reference image for each absorption image in a set as
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% a linear combination of reference images, with coefficients chosen to
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% minimize the least-squares residuals between each absorption image and
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% the optimal reference image. The coefficients are obtained by solving a
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% linear set of equations using matrix inverse by LU decomposition.
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%
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% Application of the algorithm is described in C. F. Ockeloen et al, Improved
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% detection of small atom numbers through image processing, arXiv:1007.2136 (2010).
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%
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% Syntax:
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% [optrefimages] = removefringesInImage(absimages,refimages,bgmask);
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%
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% Required inputs:
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% absimages - Absorption image data,
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% typically 16 bit grayscale images
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% refimages - Raw reference image data
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% absimages and refimages are both cell arrays containing
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% 2D array data. The number of refimages can differ from the
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% number of absimages.
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%
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% Optional inputs:
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% bgmask - Array specifying background region used,
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% 1=background, 0=data. Defaults to all ones.
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% Outputs:
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% optrefimages - Cell array of optimal reference images,
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% equal in size to absimages.
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%
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% Dependencies: none
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%
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% Authors: Shannon Whitlock, Caspar Ockeloen
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% Reference: C. F. Ockeloen, A. F. Tauschinsky, R. J. C. Spreeuw, and
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% S. Whitlock, Improved detection of small atom numbers through
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% image processing, arXiv:1007.2136
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% Email:
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% May 2009; Last revision: 11 August 2010
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% Process inputs
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% Set variables, and flatten absorption and reference images
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nimgs = size(absimages,3);
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nimgsR = size(refimages,3);
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xdim = size(absimages(:,:,1),2);
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ydim = size(absimages(:,:,1),1);
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R = single(reshape(refimages,xdim*ydim,nimgsR));
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A = single(reshape(absimages,xdim*ydim,nimgs));
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optrefimages=zeros(size(absimages)); % preallocate
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if not(exist('bgmask','var')); bgmask=ones(ydim,xdim); end
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k = find(bgmask(:)==1); % Index k specifying background region
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% Ensure there are no duplicate reference images
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% R=unique(R','rows')'; % comment this line if you run out of memory
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% Decompose B = R*R' using singular value or LU decomposition
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[L,U,p] = lu(R(k,:)'*R(k,:),'vector'); % LU decomposition
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for j=1:nimgs
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b=R(k,:)'*A(k,j);
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% Obtain coefficients c which minimise least-square residuals
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lower.LT = true; upper.UT = true;
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c = linsolve(U,linsolve(L,b(p,:),lower),upper);
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% Compute optimised reference image
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optrefimages(:,:,j)=reshape(R*c,[ydim xdim]);
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end
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end
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