Table 2. Comparison of the holographic image reconstruction runtime for a field of view of ~1 mm2 for different phase recovery approaches.
| Deep network output (STS) (Nholo=1) | Deep network output (Universal) (Nholo=1) | Multi-height phase-recovery (Nholo=2) | Multi-height phase-recovery (Nholo=3) | Multi-height phase-recovery (Nholo=4) | Multi-height phase-recovery (Nholo=5) | Multi-height phase-recovery (Nholo=6) | Multi-height phase-recovery (Nholo=7) | Multi-height phase-recovery (Nholo=8) | |
|---|---|---|---|---|---|---|---|---|---|
| Runtime (s) | 6.45 | 7.85 | 23.20 | 28.32 | 32.11 | 35.89 | 38.28 | 43.13 | 47.43 |
All the reconstructions were performed on a laptop using a single GPU (see Supplementary Information for details). Of the 6.45 s and 7.85 s required for image reconstruction from a single hologram intensity using sample-type-specific and universal neural networks, respectively, the deep neural network processing time is 3.11 s for the sample-type-specific network and 4.51 s for the universal network, while the rest of the time (that is, 3.34 s for the preprocessing stages) is used for other operations such as pixel super-resolution, auto-focusing and free space back-propagation.