Inverse scattering is the process of estimating the spatial distribution of the scattering potential of an object by measuring the scattered wavefields around it. In this paper, we consider reflection tomography of high contrast objects that commonly occurs in ground-penetrating radar, exploration geo- physics, terahertz imaging, ultrasound, and electron microscopy. Unlike conventional transmission tomography, the reflection regime is severely ill-posed since the measured wavefields contain far less spatial frequency information of the target object. We propose a constrained incremental frequency inversion frame- work that requires no side information from a background model of the object. Our framework solves a sequence of regularized least-squares subproblems that ensure consistency with the measured scattered wavefield while imposing total- variation and non-negativity constraints. We propose a proximal Quasi-Newton method to solve the resulting subproblem and devise an automatic parameter selection routine to determine the constraint of each subproblem. We validate the performance of our approach on synthetic low-resolution phantoms and with a mismatched forward model test on a high-resolution phantom.

Computational imaging, Inverse scattering, Total variation regularization, Reflection tomography, Limited data
Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA
dx.doi.org/10.1109/TCI.2020.3038171
IEEE Transactions on Computational Imaging
Computational Imaging

Kadu, A.A, Mansour, H, & Boufounos, P.T. (2020). High-contrast reflection tomography with total-variation constraints. IEEE Transactions on Computational Imaging, 6, 1523–1536. doi:10.1109/TCI.2020.3038171