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tomo/+lamino/estimate_reliability_region.m

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+% weight_sino = estimate_reliability_region(complex_projection, probe_size, subsample)
+% Estimates the region where the complex projections are good enough to be unwrapped. Assumes that outside 
+% of the measured FOV the amplitude of the object will be zero. This assumes that ptychography reconstruction
+% did not add any values, due to those pixels never been reached by any probe element. Then the region is reduced
+% by half the probe size using something akin to erosion. This function is only useful if the FOV is not square, 
+% such as the case of the elliptical FOV of laminography.
+% 
+%  Pseudo code example: 
+%  weights = imerode(abs(object) > 0, ones(probe_size/2))
+% 
+% Inputs:
+%    **complex_projection       - (3D array) complex valued reconstructions 
+%    **probe_size               - (int, int) size of the illumination probe 
+%    **subsample                - (int) subsample the resulting array to save memory
+% Outputs:
+%   ++weight_sino                 - weights, 1 for full quality, 0<=W<1 for poor regions 
+
+
+%*-----------------------------------------------------------------------*
+%|                                                                       |
+%|  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) 2018 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 weight_sino = estimate_reliability_region(complex_projection, probe_size, subsample)
+    % simple reliability estimation based on amplitude of the
+    % reconstruction 
+    
+    Npix = size(complex_projection); 
+    Npix_new = ceil(Npix(1:2)/subsample/2)*2; 
+    
+    %% SOLVE THE PROBLEM IN LOW RESOLUTION 
+    weight_sino = tomo.block_fun(@utils.interpolateFT, complex_projection,Npix_new, struct('use_GPU', false, 'use_fp16', false)); 
+    probe_size = round(probe_size .* Npix_new ./ Npix(1:2)); 
+    weight_sino =abs(weight_sino); 
+    weight_sino = single(weight_sino > 0.1*quantile(weight_sino(:),0.9)); 
+    
+    %% only CPU is supported -> gather and move abck to GPU afterwards 
+    weight_sino = gpuArray(utils.imcrop_outliers(gather(weight_sino)));     % leave only single largest compact object 
+
+    
+    [Y,X] = meshgrid(-ceil(probe_size(1)/2):floor(probe_size(1)/2), -ceil(probe_size(2)/2):floor(probe_size(2)/2));
+    probe = (X/probe_size(1)*2).^2+(Y/probe_size(2)*2).^2 < 1; 
+    
+    kernel = probe/sum(probe(:)); 
+    weight_sino = convn(weight_sino, kernel , 'same'); 
+    weight_sino = weight_sino > 0.95; 
+    weight_sino = uint8(imgaussfilt(single(weight_sino), 1)*255);    
+    
+end