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91 lines
3.6 KiB
Matlab
91 lines
3.6 KiB
Matlab
% [volume_new, update] = apply_tomo_constraints(volume, mask, lamino_angle , low_freq_protection, constrain_fun, Niter)
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% apply laminography constraints in the real space and try to refill missing cone in laminography by provided prior
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% knowledge
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% Inputs:
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% **volume - (3D array) represeting the refined volume in realspace
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% **mask - (vector, array), mask pushing pixels where mask < 1 towards zero. Can be either 3D or only for example along 3r axis ie size(mask) = [1,1,Nlayers]
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% **lamino_angle - (scalar), laminography angle from 0 to 90degrees, 90 == classical tomo, it is used to calculate the missing cone
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% **low_freq_protection - (bool), used to protect in the fourier space the central region, ie low spatial frequncies. Important when multiscale approach is used
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% **constrain_fun - anonymous function providing constrains such as positivity or material range limits
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% **Niter - number of optimization iterations
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% *returns*
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% ++volume_new refined object
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% ++update (norm(volume) - norm(update_new)) / norm(volume)
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%
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% Example:
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% see template_tomo_recons_lamino.m for working example
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function [volume_new, update] = apply_tomo_constraints(volume, mask, angles, low_freq_protection, value_max, value_min, TV_lambda, Niter)
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import utils.Garray
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Npix = size(volume);
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fft_mask = tomo.get_tomo_fourier_mask_3d( Npix, angles);
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fft_mask = Garray(fft_mask);
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if low_freq_protection
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% avoid modification of the low spatial frequencies that were
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% already refined
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fft_mask = fftshift(fft_mask);
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for i = 1:3
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grid{i} = ceil(Npix(i)/2)+[-ceil(Npix(i)/8):floor(Npix(i)/8)];
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end
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fft_mask(grid{:}) = 0;
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fft_mask = fftshift(fft_mask);
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end
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volume = Garray(volume);
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fft_split = 1;
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for iter = 1:Niter
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utils.progressbar(iter,Niter)
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volume_new = volume;
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volume_new = regularization.local_TV3D_chambolle(volume_new, TV_lambda, 10);
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% positivity constraint
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volume_new = arrayfun(@clip_range,volume_new, value_max, value_min, mask);
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%volume_new = clip_range(volume_new, value_max, value_min, mask);
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%% go to the Fourier space
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fvolume = (math.fftn_partial(Garray(volume), fft_split));
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fvolume_new = (math.fftn_partial(Garray(volume_new), fft_split));
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%% merge updated and original dataset in the fourier space
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%% use overrelaxation of the constraint to get faster convergence
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relax = 1.5;
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regularize = 0;
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fvolume = arrayfun(@relax_contraint,fvolume, fvolume_new, fft_mask, relax, regularize);
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clear fvolume_new
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%% back to the real space
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volume_new = real(math.ifftn_partial(Garray(fvolume), fft_split));
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clear fvolume
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% get difference in update
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update = gather(norm(volume(:)-volume_new(:)) ./ norm(volume(:)));
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volume = volume_new;
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end
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volume = gather(volume);
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end
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% auxiliary function for fast execution on GPU
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function fvolume = relax_contraint(fvolume, fvolume_new, fft_mask, relax, regularize)
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fvolume = fvolume .* ( 1- relax.*fft_mask) + fvolume_new .* relax.*fft_mask;
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%relax_data = 0.2;
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%fft_mask_data = 1 - fft_mask;
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%fvolume = fvolume .* ( 1- relax_data*fft_mask_data) + fvolume_new .* relax_data.*fft_mask_data;
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fvolume = fvolume .* (1 - regularize.*fft_mask);
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end
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function array = clip_range(array, max_val, min_val, mask)
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array = max(min_val, min(max_val, array)) .* mask;
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%array = max(min_val, min(max_val, array));
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end
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