mirror of
https://github.com/c-sooyoung/fold_slice.git
synced 2026-09-17 17:29:09 +09:00
91 lines
3.9 KiB
Matlab
91 lines
3.9 KiB
Matlab
% SMOOTH_EDGES takes stack of 2D images and smooths boundaries to avoid sharp edge artefacts during imshift_fft
|
||
%
|
||
% img = smooth_edges(img, win_size, dims)
|
||
%
|
||
% Inputs:
|
||
% **img - 2D stacked array, smoothing is done along first two dimensions
|
||
% **win_size - size of the smoothing region, default is 3
|
||
% **dims - list of dimensions along which will by smoothing done
|
||
% Outputs:
|
||
% ++img - smoothed array
|
||
|
||
% *-----------------------------------------------------------------------*
|
||
% | |
|
||
% | 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 img = smooth_edges(img, win_size, dims)
|
||
|
||
if nargin < 3
|
||
dims = [1,2]; % default is smooth along first 2 dimensions
|
||
end
|
||
|
||
if nargin < 2
|
||
win_size = 5;
|
||
end
|
||
|
||
try
|
||
Npix = size(img);
|
||
for i = dims
|
||
ind = {':',':',':'};
|
||
if Npix(i) <= 2*win_size ; continue; end
|
||
win_size = max(win_size, 3);
|
||
% get indices of the edge regions
|
||
ind{i} = [Npix(i)-win_size+1:Npix(i),1:win_size];
|
||
ker_size = [1,1];
|
||
ker_size(i) = win_size;
|
||
img_tmp = img(ind{:});
|
||
kernel = reshape(gausswin(win_size,2.5), ker_size);
|
||
% smooth across the image edges
|
||
img_tmp = convn(img_tmp, kernel, 'same');
|
||
% avoid boundary issues from convolution
|
||
boundary_shape = [1,1];
|
||
boundary_shape(i) = length(ind{i});
|
||
img_tmp = bsxfun(@rdivide, img_tmp, conv(ones(boundary_shape), kernel, 'same'));
|
||
img(ind{:}) = img_tmp;
|
||
end
|
||
catch err
|
||
warning('Smooth edges failed: %s', err.message)
|
||
end
|
||
|
||
end
|
||
|
||
function w = gausswin(L, a)
|
||
% Compute window according to [1]
|
||
N = L-1;
|
||
n = (0:N)'-N/2;
|
||
w = exp(-(1/2)*(a*n/(N/2)).^2);
|
||
end
|
||
|