ab_tv_diff_solve

PURPOSE ^

AB_TV_DIFF_SOLVE inverse solver for Andrea Borsic's

SYNOPSIS ^

function img= ab_tv_diff_solve( inv_model, data1, data2)

DESCRIPTION ^

 AB_TV_DIFF_SOLVE inverse solver for Andrea Borsic's
   Total Variation solver for use with difference EIT
 img= ab_tv_diff_solve( inv_model, data1, data2)
 img        => output image
 inv_model  => inverse model struct
 data1      => differential data at earlier time
 data2      => differential data at later time
 Parameters
   alpha1
   alpha2
   want_dual_variable  (set to 1 if you want access to dual)
 Termination parameters
  max_iters =  inv_model.parameters.max_iteration (default 10)
      Max number of iterations before stopping
  min change = inv_model.parameters.min_change   (default 0)
      Min Change in objective fcn (norm(y-Jx)^2 + hp*TV(x)) before stopping

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SUBFUNCTIONS ^

SOURCE CODE ^

0001 function img= ab_tv_diff_solve( inv_model, data1, data2)
0002 % AB_TV_DIFF_SOLVE inverse solver for Andrea Borsic's
0003 %   Total Variation solver for use with difference EIT
0004 % img= ab_tv_diff_solve( inv_model, data1, data2)
0005 % img        => output image
0006 % inv_model  => inverse model struct
0007 % data1      => differential data at earlier time
0008 % data2      => differential data at later time
0009 % Parameters
0010 %   alpha1
0011 %   alpha2
0012 %   want_dual_variable  (set to 1 if you want access to dual)
0013 % Termination parameters
0014 %  max_iters =  inv_model.parameters.max_iteration (default 10)
0015 %      Max number of iterations before stopping
0016 %  min change = inv_model.parameters.min_change   (default 0)
0017 %      Min Change in objective fcn (norm(y-Jx)^2 + hp*TV(x)) before stopping
0018 
0019 % (C) 2002-2009 Andrea Borsic and Andy Adler. License: GPL version 2 or version 3
0020 % $Id: ab_tv_diff_solve.m 3428 2012-07-02 20:56:41Z bgrychtol $
0021 warning('EIDORS:deprecated','AB_TV_DIFF_SOLVE is deprecated as of 06-Jun-2012. Use INV_SOLVE_TV_PDIPM instead.');
0022 
0023 
0024 p= get_params(inv_model);
0025 
0026 dva = calc_difference_data( data1, data2, inv_model.fwd_model);
0027 % TEST CODE -> Put elsewhere
0028 back_val = get_good_background(inv_model, data1);
0029 inv_model.jacobian_bkgnd.value= back_val;
0030 
0031 sol= [];
0032 for i=1:size(dva,2)
0033    [soln,dual_x]=primaldual_tvrecon_lsearch(inv_model, dva(:,i), ...
0034         p.maxiter,p.alpha1,p.alpha2, p.tol, p.beta, p.min_change);
0035 
0036    if ~p.keepiters
0037       soln=soln(:,end);
0038    end
0039 
0040    sol=[sol, soln];
0041 end
0042 
0043 img.name= 'solved by ab_tv_diff_solve';
0044 img.elem_data = sol;
0045 img.fwd_model= inv_model.fwd_model;
0046 try if inv_model.ab_tv_diff_solve.want_dual_variable
0047    img.dual_data = dual_x;
0048 end; end
0049 
0050 function p = get_params(inv_model);
0051    
0052    try   p.alpha1= inv_model.ab_tv_diff_solve.alpha1;
0053    catch p.alpha1= 1e-2;
0054    end
0055 
0056    try   p.beta= inv_model.ab_tv_diff_solve.beta;
0057    catch p.beta= 1e-4;
0058    end
0059 
0060    p.alpha2= calc_hyperparameter( inv_model);
0061 
0062    try   p.min_change = inv_model.parameters.min_change;
0063    catch p.min_change = 0;
0064    end
0065 
0066    try   p.maxiter = inv_model.parameters.max_iterations;
0067    catch p.maxiter= 10;
0068    end
0069 
0070    try   p.keepiters = inv_model.parameters.keep_iterations;
0071    catch p.keepiters= 0;
0072    end
0073 
0074    p.tol = 0; % TODO
0075 
0076 function back_val = get_good_background(inv_mdl, data1);
0077 
0078    % Create homogeneous model
0079    IM= eidors_obj('image','');
0080    IM.fwd_model= inv_mdl.fwd_model;
0081    s= ones(size(IM.fwd_model.elems,1),1);
0082    IM.elem_data= s;
0083 
0084    vsim= fwd_solve( IM);
0085    back_val=abs( data1\vsim.meas );
0086    back_val=1;
0087

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