Calculations/Data-Analyzer/+Calculator/computeFourierTransform.m

70 lines
2.5 KiB
Matlab

function [IMGFFT, IMGPR] = computeFourierTransform(I, skipPreprocessing, skipMasking, skipIntensityThresholding, skipBinarization)
% computeFourierSpectrum - Computes the 2D Fourier power spectrum
% of binarized and enhanced lattice image features, with optional central mask.
%
% Inputs:
% I - Grayscale or RGB image matrix
%
% Output:
% F_mag - 2D Fourier power spectrum (shifted)
if ~skipPreprocessing
% Preprocessing: Denoise
filtered = imgaussfilt(I, 10);
IMGPR = I - filtered; % adjust sigma as needed
else
IMGPR = I;
end
if ~skipMasking
[rows, cols] = size(IMGPR);
[X, Y] = meshgrid(1:cols, 1:rows);
% Elliptical mask parameters
cx = cols / 2;
cy = rows / 2;
% Shifted coordinates
x = X - cx;
y = Y - cy;
% Ellipse semi-axes
rx = 0.4 * cols;
ry = 0.2 * rows;
% Rotation angle in degrees -> radians
theta_deg = 30; % Adjust as needed
theta = deg2rad(theta_deg);
% Rotated ellipse equation
cos_t = cos(theta);
sin_t = sin(theta);
x_rot = (x * cos_t + y * sin_t);
y_rot = (-x * sin_t + y * cos_t);
ellipseMask = (x_rot.^2) / rx^2 + (y_rot.^2) / ry^2 <= 1;
% Apply cutout mask
IMGPR = IMGPR .* ellipseMask;
end
if ~skipIntensityThresholding
% Apply global intensity threshold mask
intensity_thresh = 0.20;
intensity_mask = IMGPR > intensity_thresh;
IMGPR = IMGPR .* intensity_mask;
end
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