Abstract: THz light field imaging inherently allows capturing the 3D A1 A2 A3 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 3D 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 3D image reconstruction accuracy and processing speed in THz light field imaging.

Acknowledgement: This work received partial funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 101019972; grant ID: https://doi.org/10.3030/101019972; Crossref Funder ID https://doi.org/10.13039/100010662). The TWS-ID02 THz camera was provided courtesy of TicWave-Solutions GmbH.

Copyright notice: This document is the accepted manuscript version that has been published in final form in:

IEEE Sensors Letters, DOI: https://doi.org/10.1109/LSENS.2024.3454567

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