The NLPCA method for stem4d

clear all;
close all;

% Set paths to the appropriate directories
path (path, '/home/willemm/ProjectsSSEC/GitLab/stem4d/matlab/reader')
path (path, '/home/willemm/ProjectsSSEC/GitLab/stem4d/matlab/NLPCA_stem4d')
path (path, '/home/willemm/ProjectsSSEC/GitLab/stem4d/matlab/NLPCA_stem4d/functions')

% Load first chunk row from 4 by 4 unit cell data archive
input_dirP_str = '/home/willemm/ProjectsSSEC/BeccaSTEM4D/s9_100nm_sto_4uc_12ucModel_dz_5nm_0p0_0_tilt/tiff/10nm';
fileN_format_str = 's9_0_0_tilt_im%03d_cbed-%d-%d-2.tif';
startB_nr_int = 0;
endB_nr_int = 39;
strideH_int = 1;
strideV_int = 40;
data_ca = readchunks_tiffs (input_dirP_str, fileN_format_str, startB_nr_int, endB_nr_int, strideH_int, strideV_int);
% img_arr_ca are the first four diffraction image rows
img_arr_ca = stack_horz (data_ca);
clear data_ca;

% Create noisy observations; scale the simulation so that the peak intensity of each unit cell is 10
Y_arr_ca = cell (size (img_arr_ca));
for row_idx = 1:4
    Y_arr_ca{row_idx} = poissrnd (10 * img_arr_ca{row_idx} / 10^(-2.9801));
end

% The denoise data
dn_img_arr_ca = cell (size (img_arr_ca));
for row_idx = 1:4
    dn_img_arr_ca{row_idx} = nlpca_stem4d_denoise (Y_arr_ca{row_idx}, true);
end

% Compare the noisy and denoised diffraction images of image chunk 0 (i = 0, j = 1) 
dsp_arr = [Y_arr_ca{2}(:,:,1),dn_img_arr_ca{2}(:,:,1)];
imagesc (dsp_arr);
colorbar ();
axis image;
caxis ([0, 3]);