928 lines
33 KiB
Matlab
928 lines
33 KiB
Matlab
%% Settings
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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 = "D:/Data - Experiment/2025/07/04/";
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run = '0018';
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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 = [1430, 2040];
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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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magnification = 23.94;
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removeFringes = false;
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ImagingMode = 'HighIntensity';
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PulseDuration = 5e-6; % in s
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% Fourier analysis settings
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% Radial Spectral Distribution
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theta_min = deg2rad(0);
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theta_max = deg2rad(180);
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N_radial_bins = 500;
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Radial_Sigma = 2;
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Radial_WindowSize = 5; % Choose an odd number for a centered moving average
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% Angular Spectral Distribution
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r_min = 10;
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r_max = 20;
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N_angular_bins = 180;
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Angular_Threshold = 75;
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Angular_Sigma = 2;
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Angular_WindowSize = 5;
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zoom_size = 50; % Zoomed-in region around center
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% scan_parameter = 'ps_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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savefolderPath = 'E:/Results - Experiment/B2.35G/';
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savefileName = 'Droplets';
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font = 'Bahnschrift';
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skipUnshuffling = true;
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if strcmp(savefileName, 'DropletsToStripes')
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scan_groups = 0:5:45;
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elseif strcmp(savefileName, 'StripesToDroplets')
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scan_groups = 45:-5:0;
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end
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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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skipMovieRender = true;
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skipSaveFigures = false;
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%% Load and 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, ImagingMode, PulseDuration), 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, 'ps_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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%% Unshuffle if necessary to do so
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if ~skipUnshuffling
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n_values = length(scan_groups);
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n_total = length(scan_parameter_values);
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% Infer number of repetitions
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n_reps = n_total / n_values;
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% Preallocate ordered arrays
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ordered_scan_values = zeros(1, n_total);
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ordered_od_imgs = cell(1, n_total);
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counter = 1;
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for rep = 1:n_reps
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for val = scan_groups
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% Find the next unused match for this val
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idx = find(scan_parameter_values == val, 1, 'first');
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% Assign and remove from list to avoid duplicates
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ordered_scan_values(counter) = scan_parameter_values(idx);
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ordered_od_imgs{counter} = od_imgs{idx};
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% Mark as used by removing
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scan_parameter_values(idx) = NaN; % NaN is safe since original values are 0:5:45
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od_imgs{idx} = []; % empty cell so it won't be matched again
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counter = counter + 1;
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end
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end
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% Now assign back
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scan_parameter_values = ordered_scan_values;
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od_imgs = ordered_od_imgs;
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end
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%% Run Fourier analysis over images
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fft_imgs = cell(1, nimgs);
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spectral_contrast = zeros(1, nimgs);
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spectral_weight = zeros(1, nimgs);
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N_shots = length(od_imgs);
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avg_ps_accum = 0;
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avg_S_k_accum = 0;
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avg_S_theta_accum = 0;
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% Pre-allocate once sizes are known (after first run)
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fft_size_known = false;
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if ~skipMovieRender
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% Create VideoWriter object for movie
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videoFile = VideoWriter([savefileName '.mp4'], 'MPEG-4');
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videoFile.Quality = 100; % Set quality to maximum (0–100)
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videoFile.FrameRate = 2; % Set the frame rate (frames per second)
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open(videoFile); % Open the video file to write
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end
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if ~skipSaveFigures
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% Define folder for saving images
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saveFolder = [savefileName '_SavedFigures'];
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if ~exist(saveFolder, 'dir')
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mkdir(saveFolder);
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end
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end
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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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% Size of original image (in pixels)
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[Ny, Nx] = size(IMG);
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% Real-space pixel size in micrometers after magnification
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dx = pixel_size / magnification;
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dy = dx; % assuming square pixels
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% Real-space axes
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x = ((1:Nx) - ceil(Nx/2)) * dx * 1E6;
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y = ((1:Ny) - ceil(Ny/2)) * dy * 1E6;
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% Reciprocal space increments (frequency domain, μm⁻¹)
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dvx = 1 / (Nx * dx);
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dvy = 1 / (Ny * dy);
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% Frequency axes
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vx = (-floor(Nx/2):ceil(Nx/2)-1) * dvx;
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vy = (-floor(Ny/2):ceil(Ny/2)-1) * dvy;
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% Wavenumber axes
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kx_full = 2 * pi * vx * 1E-6; % μm⁻¹
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ky_full = 2 * pi * vy * 1E-6;
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% Crop FFT image around center
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mid_x = floor(Nx/2);
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mid_y = floor(Ny/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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% Crop wavenumber axes to match fft_imgs{k}
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kx = kx_full(mid_x - zoom_size : mid_x + zoom_size);
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ky = ky_full(mid_y - zoom_size : mid_y + zoom_size);
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[theta_vals, S_theta] = computeAngularSpectralDistribution(fft_imgs{k}, r_min, r_max, N_angular_bins, Angular_Threshold, Angular_Sigma, []);
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[k_rho_vals, S_k] = computeRadialSpectralDistribution(fft_imgs{k}, kx, ky, theta_min, theta_max, N_radial_bins);
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S_k_smoothed = movmean(S_k, Radial_WindowSize); % % Compute moving average (use convolution) or use conv for more control
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spectral_contrast(k) = computeSpectralContrast(fft_imgs{k}, r_min, r_max, Angular_Threshold);
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spectral_weight(k) = trapz(theta_vals, S_theta);
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figure(1);
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clf
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set(gcf,'Position',[500 100 1000 800])
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t = tiledlayout(2, 2, 'TileSpacing', 'compact', 'Padding', 'compact');
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% ======= OD IMAGE (real space) =======
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ax1 = nexttile;
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imagesc(x, y, IMG)
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hold on;
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% Convert pixel grid to µm (already done: x and y axes)
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% Draw ↘ diagonal (top-left to bottom-right)
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drawODOverlays(x(1), y(1), x(end), y(end));
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% Draw ↙ diagonal (top-right to bottom-left)
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drawODOverlays(x(end), y(1), x(1), y(end));
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hold off;
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axis equal tight;
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set(gca, 'FontSize', 14, 'YDir', 'normal')
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colormap(ax1, Colormaps.inferno());
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hcb = colorbar;
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ylabel(hcb, 'Optical Density', 'Rotation', -90, 'FontSize', 14, 'FontName', font);
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xlabel('x (\mum)', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
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ylabel('y (\mum)', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
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title('OD Image', 'FontSize', 16, 'FontWeight', 'bold', 'Interpreter', 'tex', 'FontName', font);
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text(0.975, 0.975, [scan_parameter_text, num2str(scan_parameter_values(k), '%.2f'), ' G'], ...
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'Color', 'white', 'FontWeight', 'bold', 'FontSize', 14, ...
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'Interpreter', 'tex', 'Units', 'normalized', ...
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'HorizontalAlignment', 'right', 'VerticalAlignment', 'top');
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% ======= FFT POWER SPECTRUM (reciprocal space) =======
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ax2 = nexttile;
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imagesc(kx, ky, log(1 + abs(fft_imgs{k}).^2));
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axis image;
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set(gca, 'FontSize', 14, 'YDir', 'normal')
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xlabel('k_x [\mum^{-1}]', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
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ylabel('k_y [\mum^{-1}]', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
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title('Power Spectrum - S(k_x,k_y)', 'Interpreter', 'tex', ...
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'FontSize', 16, 'FontWeight', 'bold', 'FontName', font);
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colorbar;
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colormap(ax2, Colormaps.coolwarm());
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drawPSOverlays(kx, ky, r_min, r_max)
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% ======= RADIAL DISTRIBUTION (S(k)) =======
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nexttile;
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plot(k_rho_vals, S_k_smoothed, 'LineWidth', 2);
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set(gca, 'FontSize', 14, 'YScale', 'log', 'XLim', [min(k_rho_vals), max(k_rho_vals)]);
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xlabel('k_\rho [\mum^{-1}]', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
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ylabel('Magnitude (a.u.)', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
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title('Radial Spectral Distribution - S(k_\rho)', 'Interpreter', 'tex', ...
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'FontSize', 16, 'FontWeight', 'bold', 'FontName', font);
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grid on;
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% ======= ANGULAR DISTRIBUTION (S(θ)) =======
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nexttile;
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plot(theta_vals/pi, S_theta, 'LineWidth', 2);
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set(gca, 'FontSize', 14, 'YScale', 'log', 'YLim', [1E4, 1E7]);
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xlabel('\theta/\pi [rad]', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
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ylabel('Magnitude (a.u.)', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
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title('Angular Spectral Distribution - S(\theta)', 'Interpreter', 'tex', ...
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'FontSize', 16, 'FontWeight', 'bold', 'FontName', font);
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grid on; % Enable major grid
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ax = gca;
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ax.MinorGridLineStyle = ':'; % Optional: make minor grid dotted
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ax.MinorGridColor = [0.7 0.7 0.7]; % Optional: light gray minor grid color
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ax.MinorGridAlpha = 0.5; % Optional: transparency for minor grid
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ax.XMinorGrid = 'on'; % Enable minor grid for x-axis
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ax.YMinorGrid = 'on'; % Enable minor grid for y-axis (if desired)
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drawnow;
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if ~fft_size_known
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fft_sz = size(fft_imgs{k});
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N_radial_bins_used = length(S_k_smoothed);
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N_angular_bins_used = length(S_theta);
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avg_ps_accum = zeros(fft_sz);
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avg_S_k_accum = zeros(1, N_radial_bins_used);
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avg_S_theta_accum = zeros(1, N_angular_bins_used);
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fft_size_known = true;
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end
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avg_ps_accum = avg_ps_accum + abs(fft_imgs{k}).^2;
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avg_S_k_accum = avg_S_k_accum + S_k_smoothed;
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avg_S_theta_accum = avg_S_theta_accum + S_theta;
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if ~skipMovieRender
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% Capture the current frame and write it to the video
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frame = getframe(gcf); % Capture the current figure as a frame
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writeVideo(videoFile, frame); % Write the frame to the video
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end
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if ~skipSaveFigures
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% Construct a filename for each image
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fileNamePNG = fullfile(saveFolder, sprintf('fft_analysis_img_%03d.png', k));
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% Save current figure as PNG with high resolution
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print(gcf, fileNamePNG, '-dpng', '-r100'); % 300 dpi for high quality
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end
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if skipMovieRender & skipSaveFigures
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pause(0.5);
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end
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end
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if ~skipMovieRender
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% Close the video file
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close(videoFile);
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end
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%% ===== Final Averages =====
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avg_ps = avg_ps_accum / N_shots;
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avg_S_k = avg_S_k_accum / N_shots;
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avg_S_theta = avg_S_theta_accum / N_shots;
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% Generate figure with 3 subplots
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figure('Name', 'Average Spectral Analysis', 'Position', [400 200 1200 400]);
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tavg = tiledlayout(1, 3, 'TileSpacing', 'compact', 'Padding', 'compact');
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% ==== 1. Average FFT Power Spectrum ====
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nexttile;
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imagesc(kx, ky, log(1 + avg_ps));
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axis image;
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set(gca, 'FontSize', 14, 'YDir', 'normal')
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xlabel('k_x [\mum^{-1}]', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
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ylabel('k_y [\mum^{-1}]', 'Interpreter', 'tex', 'FontSize', 14, 'FontName', font);
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title('Average Power Spectrum', 'FontSize', 16, 'FontWeight', 'bold');
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colorbar;
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colormap(Colormaps.coolwarm());
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% ==== 2. Average Radial Spectral Distribution ====
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nexttile;
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plot(k_rho_vals, avg_S_k, 'LineWidth', 2);
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xlabel('k_\rho [\mum^{-1}]', 'Interpreter', 'tex', 'FontSize', 14);
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ylabel('Magnitude (a.u.)', 'Interpreter', 'tex', 'FontSize', 14);
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title('Average S(k_\rho)', 'FontSize', 16, 'FontWeight', 'bold');
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set(gca, 'FontSize', 14, 'YScale', 'log', 'XLim', [min(k_rho_vals), max(k_rho_vals)]);
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grid on;
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% ==== 3. Average Angular Spectral Distribution ====
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nexttile;
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plot(theta_vals/pi, avg_S_theta, 'LineWidth', 2);
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xlabel('\theta/\pi [rad]', 'Interpreter', 'tex', 'FontSize', 14);
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ylabel('Magnitude (a.u.)', 'Interpreter', 'tex', 'FontSize', 14);
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title('Average S(\theta)', 'FontSize', 16, 'FontWeight', 'bold');
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set(gca, 'FontSize', 14, 'YScale', 'log');
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grid on;
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ax = gca;
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ax.XMinorGrid = 'on';
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ax.YMinorGrid = '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
|
||
% Adaptive binarization and cleanup
|
||
IMGPR = imbinarize(IMGPR, 'adaptive', 'Sensitivity', 0.0);
|
||
IMGPR = imdilate(IMGPR, strel('disk', 2));
|
||
IMGPR = imerode(IMGPR, strel('disk', 1));
|
||
IMGPR = imfill(IMGPR, 'holes');
|
||
F = fft2(double(IMGPR)); % Compute 2D Fourier Transform
|
||
IMGFFT = abs(fftshift(F))'; % Shift zero frequency to center
|
||
else
|
||
F = fft2(double(IMGPR)); % Compute 2D Fourier Transform
|
||
IMGFFT = abs(fftshift(F))'; % Shift zero frequency to center
|
||
end
|
||
end
|
||
|
||
function [k_rho_vals, S_radial] = computeRadialSpectralDistribution(IMGFFT, kx, ky, thetamin, thetamax, num_bins)
|
||
% IMGFFT : 2D FFT image (fftshifted and cropped)
|
||
% kx, ky : 1D physical wavenumber axes [μm⁻¹] matching FFT size
|
||
% thetamin : Minimum angle (in radians)
|
||
% thetamax : Maximum angle (in radians)
|
||
% num_bins : Number of radial bins
|
||
|
||
[KX, KY] = meshgrid(kx, ky);
|
||
K_rho = sqrt(KX.^2 + KY.^2);
|
||
Theta = atan2(KY, KX);
|
||
|
||
if thetamin < thetamax
|
||
angle_mask = (Theta >= thetamin) & (Theta <= thetamax);
|
||
else
|
||
angle_mask = (Theta >= thetamin) | (Theta <= thetamax);
|
||
end
|
||
|
||
power_spectrum = abs(IMGFFT).^2;
|
||
|
||
r_min = min(K_rho(angle_mask));
|
||
r_max = max(K_rho(angle_mask));
|
||
r_edges = linspace(r_min, r_max, num_bins + 1);
|
||
k_rho_vals = 0.5 * (r_edges(1:end-1) + r_edges(2:end));
|
||
S_radial = zeros(1, num_bins);
|
||
|
||
for i = 1:num_bins
|
||
r_low = r_edges(i);
|
||
r_high = r_edges(i + 1);
|
||
radial_mask = (K_rho >= r_low) & (K_rho < r_high);
|
||
full_mask = radial_mask & angle_mask;
|
||
S_radial(i) = sum(power_spectrum(full_mask));
|
||
end
|
||
end
|
||
|
||
function [theta_vals, S_theta] = computeAngularSpectralDistribution(IMGFFT, r_min, r_max, num_bins, threshold, sigma, windowSize)
|
||
% Apply threshold to isolate strong peaks
|
||
IMGFFT(IMGFFT < threshold) = 0;
|
||
|
||
% Prepare polar coordinates
|
||
[ny, nx] = size(IMGFFT);
|
||
[X, Y] = meshgrid(1:nx, 1:ny);
|
||
cx = ceil(nx/2);
|
||
cy = ceil(ny/2);
|
||
R = sqrt((X - cx).^2 + (Y - cy).^2);
|
||
Theta = atan2(Y - cy, X - cx); % range [-pi, pi]
|
||
|
||
% Choose radial band
|
||
radial_mask = (R >= r_min) & (R <= r_max);
|
||
|
||
% Initialize angular structure factor
|
||
S_theta = zeros(1, num_bins);
|
||
theta_vals = linspace(0, pi, num_bins);
|
||
|
||
% Loop through angle bins
|
||
for i = 1:num_bins
|
||
angle_start = (i-1) * pi / num_bins;
|
||
angle_end = i * pi / num_bins;
|
||
angle_mask = (Theta >= angle_start & Theta < angle_end);
|
||
bin_mask = radial_mask & angle_mask;
|
||
fft_angle = IMGFFT .* bin_mask;
|
||
S_theta(i) = sum(sum(abs(fft_angle).^2));
|
||
end
|
||
|
||
% Smooth using either Gaussian or moving average
|
||
if exist('sigma', 'var') && ~isempty(sigma)
|
||
% Gaussian convolution
|
||
half_width = ceil(3 * sigma);
|
||
x = -half_width:half_width;
|
||
gauss_kernel = exp(-x.^2 / (2 * sigma^2));
|
||
gauss_kernel = gauss_kernel / sum(gauss_kernel);
|
||
% Circular convolution
|
||
S_theta = conv([S_theta(end-half_width+1:end), S_theta, S_theta(1:half_width)], ...
|
||
gauss_kernel, 'same');
|
||
S_theta = S_theta(half_width+1:end-half_width);
|
||
elseif exist('windowSize', 'var') && ~isempty(windowSize)
|
||
% Moving average via convolution (circular)
|
||
pad = floor(windowSize / 2);
|
||
kernel = ones(1, windowSize) / windowSize;
|
||
S_theta = conv([S_theta(end-pad+1:end), S_theta, S_theta(1:pad)], kernel, 'same');
|
||
S_theta = S_theta(pad+1:end-pad);
|
||
end
|
||
end
|
||
|
||
function contrast = computeSpectralContrast(IMGFFT, r_min, r_max, threshold)
|
||
% Apply threshold to isolate strong peaks
|
||
IMGFFT(IMGFFT < threshold) = 0;
|
||
|
||
% Prepare polar coordinates
|
||
[ny, nx] = size(IMGFFT);
|
||
[X, Y] = meshgrid(1:nx, 1:ny);
|
||
cx = ceil(nx/2);
|
||
cy = ceil(ny/2);
|
||
R = sqrt((X - cx).^2 + (Y - cy).^2);
|
||
|
||
% Ring region (annulus) mask
|
||
ring_mask = (R >= r_min) & (R <= r_max);
|
||
|
||
% Squared magnitude in the ring
|
||
ring_power = abs(IMGFFT).^2 .* ring_mask;
|
||
|
||
% Maximum power in the ring
|
||
ring_max = max(ring_power(:));
|
||
|
||
% Power at the DC component
|
||
dc_power = abs(IMGFFT(cy, cx))^2;
|
||
|
||
% Avoid division by zero
|
||
if dc_power == 0
|
||
contrast = Inf; % or NaN or 0, depending on how you want to handle this
|
||
else
|
||
contrast = ring_max / dc_power;
|
||
end
|
||
end
|
||
|
||
function ret = getBkgOffsetFromCorners(img, x_fraction, y_fraction)
|
||
% image must be a 2D numerical array
|
||
[dim1, dim2] = size(img);
|
||
|
||
s1 = img(1:round(dim1 * y_fraction), 1:round(dim2 * x_fraction));
|
||
s2 = img(1:round(dim1 * y_fraction), round(dim2 - dim2 * x_fraction):dim2);
|
||
s3 = img(round(dim1 - dim1 * y_fraction):dim1, 1:round(dim2 * x_fraction));
|
||
s4 = img(round(dim1 - dim1 * y_fraction):dim1, round(dim2 - dim2 * x_fraction):dim2);
|
||
|
||
ret = mean([mean(s1(:)), mean(s2(:)), mean(s3(:)), mean(s4(:))]);
|
||
end
|
||
|
||
function ret = subtractBackgroundOffset(img, fraction)
|
||
% Remove the background from the image.
|
||
% :param dataArray: The image
|
||
% :type dataArray: xarray DataArray
|
||
% :param x_fraction: The fraction of the pixels used in x axis
|
||
% :type x_fraction: float
|
||
% :param y_fraction: The fraction of the pixels used in y axis
|
||
% :type y_fraction: float
|
||
% :return: The image after removing background
|
||
% :rtype: xarray DataArray
|
||
|
||
x_fraction = fraction(1);
|
||
y_fraction = fraction(2);
|
||
offset = getBkgOffsetFromCorners(img, x_fraction, y_fraction);
|
||
ret = img - offset;
|
||
end
|
||
|
||
function ret = cropODImage(img, center, span)
|
||
% Crop the image according to the region of interest (ROI).
|
||
% :param dataSet: The images
|
||
% :type dataSet: xarray DataArray or DataSet
|
||
% :param center: The center of region of interest (ROI)
|
||
% :type center: tuple
|
||
% :param span: The span of region of interest (ROI)
|
||
% :type span: tuple
|
||
% :return: The cropped images
|
||
% :rtype: xarray DataArray or DataSet
|
||
|
||
x_start = floor(center(1) - span(1) / 2);
|
||
x_end = floor(center(1) + span(1) / 2);
|
||
y_start = floor(center(2) - span(2) / 2);
|
||
y_end = floor(center(2) + span(2) / 2);
|
||
|
||
ret = img(y_start:y_end, x_start:x_end);
|
||
end
|
||
|
||
function imageOD = calculateODImage(imageAtom, imageBackground, imageDark, mode, exposureTime)
|
||
%CALCULATEODIMAGE Calculates the optical density (OD) image for absorption imaging.
|
||
%
|
||
% imageOD = calculateODImage(imageAtom, imageBackground, imageDark, mode, exposureTime)
|
||
%
|
||
% Inputs:
|
||
% imageAtom - Image with atoms
|
||
% imageBackground - Image without atoms
|
||
% imageDark - Image without light
|
||
% mode - 'LowIntensity' (default) or 'HighIntensity'
|
||
% exposureTime - Required only for 'HighIntensity' [in seconds]
|
||
%
|
||
% Output:
|
||
% imageOD - Computed OD image
|
||
%
|
||
|
||
arguments
|
||
imageAtom (:,:) {mustBeNumeric}
|
||
imageBackground (:,:) {mustBeNumeric}
|
||
imageDark (:,:) {mustBeNumeric}
|
||
mode char {mustBeMember(mode, {'LowIntensity', 'HighIntensity'})} = 'LowIntensity'
|
||
exposureTime double = NaN
|
||
end
|
||
|
||
% Compute numerator and denominator
|
||
numerator = imageBackground - imageDark;
|
||
denominator = imageAtom - imageDark;
|
||
|
||
% Avoid division by zero
|
||
numerator(numerator == 0) = 1;
|
||
denominator(denominator == 0) = 1;
|
||
|
||
% Calculate OD based on mode
|
||
switch mode
|
||
case 'LowIntensity'
|
||
imageOD = -log(abs(denominator ./ numerator));
|
||
|
||
case 'HighIntensity'
|
||
if isnan(exposureTime)
|
||
error('Exposure time must be provided for HighIntensity mode.');
|
||
end
|
||
imageOD = abs(denominator ./ numerator);
|
||
imageOD = -log(imageOD) + (numerator - denominator) ./ (7000 * (exposureTime / 5e-6));
|
||
end
|
||
|
||
end
|
||
|
||
function drawODOverlays(x1, y1, x2, y2)
|
||
|
||
% Parameters
|
||
tick_spacing = 10; % µm between ticks
|
||
tick_length = 2; % µm tick mark length
|
||
line_color = [0.5 0.5 0.5];
|
||
tick_color = [0.5 0.5 0.5];
|
||
font_size = 10;
|
||
|
||
% Vector from start to end
|
||
dx = x2 - x1;
|
||
dy = y2 - y1;
|
||
L = sqrt(dx^2 + dy^2);
|
||
|
||
% Unit direction vector along diagonal
|
||
ux = dx / L;
|
||
uy = dy / L;
|
||
|
||
% Perpendicular unit vector for ticks
|
||
perp_ux = -uy;
|
||
perp_uy = ux;
|
||
|
||
% Midpoint (center)
|
||
xc = (x1 + x2) / 2;
|
||
yc = (y1 + y2) / 2;
|
||
|
||
% Number of positive and negative ticks
|
||
n_ticks = floor(L / (2 * tick_spacing));
|
||
|
||
% Draw main diagonal line
|
||
plot([x1 x2], [y1 y2], '--', 'Color', line_color, 'LineWidth', 1.2);
|
||
|
||
for i = -n_ticks:n_ticks
|
||
d = i * tick_spacing;
|
||
xt = xc + d * ux;
|
||
yt = yc + d * uy;
|
||
|
||
% Tick line endpoints
|
||
xt1 = xt - 0.5 * tick_length * perp_ux;
|
||
yt1 = yt - 0.5 * tick_length * perp_uy;
|
||
xt2 = xt + 0.5 * tick_length * perp_ux;
|
||
yt2 = yt + 0.5 * tick_length * perp_uy;
|
||
|
||
% Draw tick
|
||
plot([xt1 xt2], [yt1 yt2], '--', 'Color', tick_color, 'LineWidth', 1);
|
||
|
||
% Label: centered at tick, offset slightly along diagonal
|
||
if d ~= 0
|
||
text(xt, yt, sprintf('%+d', d), ...
|
||
'Color', tick_color, ...
|
||
'FontSize', font_size, ...
|
||
'HorizontalAlignment', 'center', ...
|
||
'VerticalAlignment', 'bottom', ...
|
||
'Rotation', atan2d(dy, dx));
|
||
end
|
||
|
||
end
|
||
end
|
||
|
||
function drawPSOverlays(kx, ky, r_min, r_max)
|
||
% drawFFTOverlays - Draw overlays on existing FFT plot:
|
||
% - Radial lines every 30°
|
||
% - Annular highlight with white (upper half) and gray (lower half) circles between r_min and r_max
|
||
% - Horizontal white bands at ky=0 in annulus region
|
||
% - Scale ticks and labels every 1 μm⁻¹ along each radial line
|
||
%
|
||
% Inputs:
|
||
% kx, ky - reciprocal space vectors (μm⁻¹)
|
||
% r_min - inner annulus radius offset index (integer)
|
||
% r_max - outer annulus radius offset index (integer)
|
||
%
|
||
% Example:
|
||
% hold on;
|
||
% drawFFTOverlays(kx, ky, 10, 30);
|
||
|
||
hold on
|
||
|
||
% === Overlay Radial Lines + Scales ===
|
||
[kx_grid, ky_grid] = meshgrid(kx, ky);
|
||
[~, kr_grid] = cart2pol(kx_grid, ky_grid); % kr_grid in μm⁻¹
|
||
|
||
max_kx = max(kx);
|
||
max_ky = max(ky);
|
||
|
||
for angle = 0 : pi/6 : pi
|
||
x_line = [0, max_kx] * cos(angle);
|
||
y_line = [0, max_ky] * sin(angle);
|
||
|
||
% Plot radial lines
|
||
plot(x_line, y_line, '--', 'Color', [0.5 0.5 0.5], 'LineWidth', 1.2);
|
||
plot(x_line, -y_line, '--', 'Color', [0.5 0.5 0.5], 'LineWidth', 1.2);
|
||
|
||
% Draw scale ticks along positive radial line
|
||
drawTicksAlongLine(0, 0, x_line(2), y_line(2));
|
||
|
||
% Draw scale ticks along negative radial line (reflect y)
|
||
drawTicksAlongLine(0, 0, x_line(2), -y_line(2));
|
||
end
|
||
|
||
% === Overlay Annular Highlight: White (r_min to r_max), Gray elsewhere ===
|
||
theta_full = linspace(0, 2*pi, 500);
|
||
|
||
center_x = ceil(size(kr_grid, 2) / 2);
|
||
center_y = ceil(size(kr_grid, 1) / 2);
|
||
|
||
k_min = kr_grid(center_y, center_x + r_min);
|
||
k_max = kr_grid(center_y, center_x + r_max);
|
||
|
||
% Upper half: white dashed circles
|
||
x1_upper = k_min * cos(theta_full(theta_full <= pi));
|
||
y1_upper = k_min * sin(theta_full(theta_full <= pi));
|
||
x2_upper = k_max * cos(theta_full(theta_full <= pi));
|
||
y2_upper = k_max * sin(theta_full(theta_full <= pi));
|
||
plot(x1_upper, y1_upper, 'k--', 'LineWidth', 1.2);
|
||
plot(x2_upper, y2_upper, 'k--', 'LineWidth', 1.2);
|
||
|
||
% Lower half: gray dashed circles
|
||
x1_lower = k_min * cos(theta_full(theta_full > pi));
|
||
y1_lower = k_min * sin(theta_full(theta_full > pi));
|
||
x2_lower = k_max * cos(theta_full(theta_full > pi));
|
||
y2_lower = k_max * sin(theta_full(theta_full > pi));
|
||
plot(x1_lower, y1_lower, '--', 'Color', [0.5 0.5 0.5], 'LineWidth', 1.0);
|
||
plot(x2_lower, y2_lower, '--', 'Color', [0.5 0.5 0.5], 'LineWidth', 1.0);
|
||
|
||
% === Highlight horizontal band across k_y = 0 ===
|
||
x_vals = kx;
|
||
xW1 = x_vals((x_vals >= -k_max) & (x_vals < -k_min));
|
||
xW2 = x_vals((x_vals > k_min) & (x_vals <= k_max));
|
||
|
||
plot(xW1, zeros(size(xW1)), 'k--', 'LineWidth', 1.2);
|
||
plot(xW2, zeros(size(xW2)), 'k--', 'LineWidth', 1.2);
|
||
|
||
hold off
|
||
|
||
|
||
% --- Nested helper function to draw ticks along a radial line ---
|
||
function drawTicksAlongLine(x_start, y_start, x_end, y_end)
|
||
% Tick parameters
|
||
tick_spacing = 1; % spacing between ticks in μm⁻¹
|
||
tick_length = 0.05 * sqrt((x_end - x_start)^2 + (y_end - y_start)^2); % relative tick length
|
||
line_color = [0.5 0.5 0.5];
|
||
tick_color = [0.5 0.5 0.5];
|
||
font_size = 8;
|
||
|
||
% Vector along the line
|
||
dx = x_end - x_start;
|
||
dy = y_end - y_start;
|
||
L = sqrt(dx^2 + dy^2);
|
||
ux = dx / L;
|
||
uy = dy / L;
|
||
|
||
% Perpendicular vector for ticks
|
||
perp_ux = -uy;
|
||
perp_uy = ux;
|
||
|
||
% Number of ticks (from 0 up to max length)
|
||
n_ticks = floor(L / tick_spacing);
|
||
|
||
for i = 1:n_ticks
|
||
% Position of tick along the line
|
||
xt = x_start + i * tick_spacing * ux;
|
||
yt = y_start + i * tick_spacing * uy;
|
||
|
||
% Tick endpoints
|
||
xt1 = xt - 0.5 * tick_length * perp_ux;
|
||
yt1 = yt - 0.5 * tick_length * perp_uy;
|
||
xt2 = xt + 0.5 * tick_length * perp_ux;
|
||
yt2 = yt + 0.5 * tick_length * perp_uy;
|
||
|
||
% Draw tick
|
||
plot([xt1 xt2], [yt1 yt2], '-', 'Color', tick_color, 'LineWidth', 1);
|
||
|
||
% Label with distance (integer)
|
||
text(xt, yt, sprintf('%d', i), ...
|
||
'Color', tick_color, ...
|
||
'FontSize', font_size, ...
|
||
'HorizontalAlignment', 'center', ...
|
||
'VerticalAlignment', 'bottom', ...
|
||
'Rotation', atan2d(dy, dx));
|
||
end
|
||
end
|
||
|
||
end
|
||
|
||
function [optrefimages] = removefringesInImage(absimages, refimages, bgmask)
|
||
% removefringesInImage - Fringe removal and noise reduction from absorption images.
|
||
% Creates an optimal reference image for each absorption image in a set as
|
||
% a linear combination of reference images, with coefficients chosen to
|
||
% minimize the least-squares residuals between each absorption image and
|
||
% the optimal reference image. The coefficients are obtained by solving a
|
||
% linear set of equations using matrix inverse by LU decomposition.
|
||
%
|
||
% Application of the algorithm is described in C. F. Ockeloen et al, Improved
|
||
% detection of small atom numbers through image processing, arXiv:1007.2136 (2010).
|
||
%
|
||
% Syntax:
|
||
% [optrefimages] = removefringesInImage(absimages,refimages,bgmask);
|
||
%
|
||
% Required inputs:
|
||
% absimages - Absorption image data,
|
||
% typically 16 bit grayscale images
|
||
% refimages - Raw reference image data
|
||
% absimages and refimages are both cell arrays containing
|
||
% 2D array data. The number of refimages can differ from the
|
||
% number of absimages.
|
||
%
|
||
% Optional inputs:
|
||
% bgmask - Array specifying background region used,
|
||
% 1=background, 0=data. Defaults to all ones.
|
||
% Outputs:
|
||
% optrefimages - Cell array of optimal reference images,
|
||
% equal in size to absimages.
|
||
%
|
||
|
||
% Dependencies: none
|
||
%
|
||
% Authors: Shannon Whitlock, Caspar Ockeloen
|
||
% Reference: C. F. Ockeloen, A. F. Tauschinsky, R. J. C. Spreeuw, and
|
||
% S. Whitlock, Improved detection of small atom numbers through
|
||
% image processing, arXiv:1007.2136
|
||
% Email:
|
||
% May 2009; Last revision: 11 August 2010
|
||
|
||
% Process inputs
|
||
|
||
% Set variables, and flatten absorption and reference images
|
||
nimgs = size(absimages,3);
|
||
nimgsR = size(refimages,3);
|
||
xdim = size(absimages(:,:,1),2);
|
||
ydim = size(absimages(:,:,1),1);
|
||
|
||
R = single(reshape(refimages,xdim*ydim,nimgsR));
|
||
A = single(reshape(absimages,xdim*ydim,nimgs));
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||
optrefimages=zeros(size(absimages)); % preallocate
|
||
|
||
if not(exist('bgmask','var')); bgmask=ones(ydim,xdim); end
|
||
k = find(bgmask(:)==1); % Index k specifying background region
|
||
|
||
% Ensure there are no duplicate reference images
|
||
% R=unique(R','rows')'; % comment this line if you run out of memory
|
||
|
||
% Decompose B = R*R' using singular value or LU decomposition
|
||
[L,U,p] = lu(R(k,:)'*R(k,:),'vector'); % LU decomposition
|
||
|
||
for j=1:nimgs
|
||
b=R(k,:)'*A(k,j);
|
||
% Obtain coefficients c which minimise least-square residuals
|
||
lower.LT = true; upper.UT = true;
|
||
c = linsolve(U,linsolve(L,b(p,:),lower),upper);
|
||
|
||
% Compute optimised reference image
|
||
optrefimages(:,:,j)=reshape(R*c,[ydim xdim]);
|
||
end
|
||
end |