Coarse-to-Fine Sparse 3-D Reconstruction in THz Light Field Imaging

Terahertz (THz) light field imaging inherently allows capturing the 3-D geometry of a target but at the cost of an increased data volume. Compressive sensing techniques are instrumental in minimizing data acquisition requirements. However, they often rely on computationally expensive sparse reconstruction approaches with high memory footprint. This research introduces an advanced coarse-to-fine (CTF) sparse 3-D reconstruction strategy aimed at enhancing the precision of reconstructed images while significantly reducing computational load and memory footprint. By employing a sequence of sensing matrices of increasing resolution, our approach avoids falling into an ill-conditioned inversion and strikes a balance between reconstruction quality and computational efficiency. We demonstrate the effectiveness of this CTF strategy through its integration with several established algorithms, including basis pursuit (BP), fast iterative shrinkage-threshold algorithm (FISTA), and others. The results showcase the potential of the CTF approach to improve 3-D image reconstruction accuracy and processing speed in THz light field imaging.