Ten lectures on wavelets
Superresolution via sparsity constraints
SIAM Journal on Mathematical Analysis
Recovery Algorithms for Vector-Valued Data with Joint Sparsity Constraints
SIAM Journal on Numerical Analysis
Adaptive iterative thresholding algorithms for magnetoencephalography (MEG)
Journal of Computational and Applied Mathematics
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Neuroelectric current localization from combined EEG/MEG data
Proceedings of the 7th international conference on Curves and Surfaces
| Hi-index | 0.00 |
Magnetic tomography is an ill-posed and ill-conditioned inverse problem since, in general, the solution is non-unique and the measured magnetic field is affected by high noise. We use a joint sparsity constraint to regularize the magnetic inverse problem. This leads to a minimization problem whose solution can be approximated by an iterative thresholded Landweber algorithm. The algorithm is proved to be convergent and an error estimate is also given. Numerical tests on a bidimensional problem show that our algorithm outperforms Tikhonov regularization when the measurements are distorted by high noise.