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tomo/utils/projection_propagation_optimization.m
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% PROJECTION_PROPAGATION_OPTIMIZATION Estimate optimal propagation distance to minimize amplitude
%
% optimum = projection_propagation_optimization( stack_object, angles, range, ROI, par)
%
% Inputs:
% **stack_object - complex projections
% **angles - projection angles
% **range - scanning range
% **ROI - region of interest, cell
% **par - parameter structure
%*-----------------------------------------------------------------------*
%|                                                                       |
%|  Except where otherwise noted, this work is licensed under a          |
%|  Creative Commons Attribution-NonCommercial-ShareAlike 4.0            |
%|  International (CC BY-NC-SA 4.0) license.                             |
%|                                                                       |
%|  Copyright (c) 2017 by Paul Scherrer Institute (http://www.psi.ch)    |
%|                                                                       |
%|      Author: CXS group, PSI  |
%*-----------------------------------------------------------------------*
% You may use this code with the following provisions:
%
% If the code is fully or partially redistributed, or rewritten in another
% computing language this notice should be included in the redistribution.
%
% If this code, or subfunctions or parts of it, is used for research in a
% publication or if it is fully or partially rewritten for another
% computing language the authors and institution should be acknowledged
% in written form in the publication: “Data processing was carried out
% using the “cSAXS matlab package” developed by the CXS group,
% Paul Scherrer Institut, Switzerland.”
% Variations on the latter text can be incorporated upon discussion with
% the CXS group if needed to more specifically reflect the use of the package
% for the published work.
%
% A publication that focuses on describing features, or parameters, that
% are already existing in the code should be first discussed with the
% authors.
%
% This code and subroutines are part of a continuous development, they
% are provided “as they are” without guarantees or liability on part
% of PSI or the authors. It is the user responsibility to ensure its
% proper use and the correctness of the results.
function optimum = projection_propagation_optimization( stack_object, angles, range, ROI, par)
disp('Estimation of optimal focus')
propagation_score = tomo.block_fun(@scan_propagation, stack_object, range, par, struct('use_fp16', false,'use_GPU', true, 'ROI', {ROI}, 'GPU_list', par.GPU_list));
propagation_score = propagation_score - mean(mean(propagation_score,1),3);
propagation_score = propagation_score ./ std(std(propagation_score,[],1),[],3);
score = squeeze(trimmean(propagation_score,10,'round',3));
subplot(1,3,1)
plot(range'*1e6,squeeze(propagation_score(:,1,:)) , '-')
title('Variance amplitude')
xlabel('Propagation distance [\mum]')
ylabel('Normalized local variance')
grid on
hold all
plotting.vline(1e6*range(math.argmin(score(:,1))))
hold off
subplot(1,3,2)
plot(range*1e6,squeeze(propagation_score(:,2,:)) , '-')
title('Variance phase')
xlabel('Propagation distance [\mum]')
ylabel('Normalized local variance')
grid on
hold all
plotting.vline(1e6*range(math.argmax(score(:,2))), 'r:', 'Optimal propagation')
hold off
optimum = sort([math.argmax(score(:,2)),math.argmin(score(:,1))]);
optimum = 1e6*range(optimum);
%suptitle(sprintf('Optimal propagation %3.1f - %3.1f um',optimum ))
sprintf('Optimal propagation %3.1f - %3.1f um',optimum )
propagation_score(:,2,:) = -propagation_score(:,2,:);
propagation_score = propagation_score ./ min(propagation_score,[],1);
[optim_shift_amp,ind] = find(squeeze(propagation_score(:,1,:)) == 1);
[~,uind] = unique(ind);
optim_shift_amp = optim_shift_amp(uind);
[optim_shift_phase,ind] = find(squeeze(propagation_score(:,2,:)) == 1);
[~,uind] = unique(ind);
optim_shift_phase = optim_shift_phase(uind);
subplot(2,3,3)
plot(1e6*range(optim_shift_amp)+randn(size(optim_shift_amp))'*0.01, 1e6*range(optim_shift_phase)+randn(size(optim_shift_amp))'*0.01, 'o');
title(sprintf('Correlation between phase/amplitude %3.2f', corr(optim_shift_amp, optim_shift_phase)))
axis equal square
grid on
xlabel('Optimal shift from amplitude')
ylabel('Optimal shift from phase')
subplot(2,3,6)
hold all
plot(angles, 1e6*range(optim_shift_amp), '.')
plot(angles, 1e6*range(optim_shift_phase), '.')
hold off
xlabel('Angles [deg]')
ylabel('Optimal offset [\mum]')
legend({'Amplitude', 'Phase'})
axis tight
grid on
% optimum = (range(optim_shift_amp) + range(optim_shift_phase))/2;
optimum = range(optim_shift_amp);
end
function variance = scan_propagation(stack_object, range, par)
Nproj = size(stack_object, 3);
for kk = 1:length(range)
shift = range(kk);
stack_object_prop = utils.prop_free_nf(stack_object, par.lambda, shift, par.pixel_size);
stack_object_amp = abs(stack_object_prop);
stack_object_phase = -math.unwrap2D_fft2(stack_object_prop,par.air_gap,0,1,0);
clear stack_object_prop
% estimate local variance for amplitude
stack_object_amp = stack_object_amp-utils.imgaussfilt2_fft(stack_object_amp,3);
stack_object_amp = reshape(stack_object_amp,[],Nproj);
variance(kk,1,:) = std(stack_object_amp);
% estimate local variance for phase
stack_object_phase = stack_object_phase-utils.imgaussfilt2_fft(stack_object_phase,3);
stack_object_phase = reshape(stack_object_phase,[],Nproj);
variance(kk,2,:) = std(stack_object_phase);
end
end