An iterative algorithm with joint sparsity constraints for magnetic tomography
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.






