A LSQR-type method provides a computationally efficient automated optimal choice of regularization parameter in diffuse optical tomography
Matlab Codes* : (requires NIRFAST)
#Matlab Implementation of LSQR based algorithm (proposed): reconstruct_cw_lsqr.m (requires objective function: opt_lambda_cw.m and Lanczos Bidiagonalization function: lsqr_b_hybrid.m**)
#Matlab Implementation of L-curve based algorithm (traditional method): reconstruct_cw_l_curve.m (requires Regularization Tools**)
#Matlab Implementation of GCV based algorithm (traditional method): reconstruct_cw_OGCV.m (requires Regularization Tools**)
#Matlab Implementation of MRM based algorithm (traditional method): reconstruct_stnd_cw_OMRM.m
This Matlab code is used as part of the work presented in:
Jaya Prakash and Phaneendra K. Yalavarthy, “A LSQR-type method provides a computationally efficient automated optimal choice of regularization parameter in diffuse optical tomography," Medical Physics, 40(3), 033101 (2013).
Created on: Sep 9, 2012
Updated on: Sep 11, 2012
* The code does not come with any guarantees and can be freely used for any purpose.
** Adapted from Regularization Tools (Version: 4.1)
The codes for this can be found at : https://github.com/Medical-Imaging-Group/A-LSQR-type-method-provides-a-computationally-efficient-automated-optimal-choice-of-regularization-p