Spatio-Temporal Super-Resolution for CS-Based ToF 3D Imaging

Time-of-Flight cameras, especially the ones featuring sensors built upon macro-pixel architectures, are generally characterized by lower resolutions than other imaging modalities. In this work, we propose a practical single-frame super-resolution scheme to surpass this limitation by exploiting the architecture of a laboratory-designed macro-pixel ultra-high-speed image sensor. In addition, we propose a two-step sparsity-aware greedy algorithm for the recovery of the signal in the time (depth) domain. The main contribution of our algorithm is the introduction of a preliminary screening step to define a set of feasible support candidates in which we replace the demodulation functions, the assembly of which yields the sensing matrix, by the theoretical binary codes they are built upon. We empirically demonstrate that our algorithm improves the recovery performance with respect to Order Recursive Matching Pursuit for sparsity
$\leq 2$
.