Computationally efficient processing of in situ underwater digital holograms

Abstract

Underwater digital in-line holography can provide high-resolution, in situ imagery of marine particles and offers many advantages over alternative measurement approaches. However, processing of holograms requires computationally expensive reconstruction and processing, and computational cost increases with the size of the imaging volume. In this work, a processing pipeline is developed to extract targets from holograms where target distribution is relatively sparse without reconstruction of the full hologram. This is motivated by the desire to efficiently extract quantitative estimates of plankton abundance from a data set (>300,000 holograms) collected in the Northwest Atlantic using a large-volume holographic camera. First, holograms with detectable targets are selected using a transfer learning approach. This was critical as a subset of the holograms were impacted by optical turbulence, which obscured target detection. Then, target diffraction patterns are detected in the hologram. Finally, targets are reconstructed and focused using only a small region of the hologram around the detected diffraction pattern. A search algorithm is employed to select distances for reconstruction, reducing the number of reconstructions required for 1 mm focus precision from 1000 to 31. When compared with full reconstruction techniques, this method detects 99% of particles larger than 0.1 mm2, a size class which includes most copepods and larger particles of marine snow, and 85% of those targets are sufficiently focused for classification. This approach requires 1% of the processing time required to compute full reconstructions, making processing of long time-series, large imaging volume holographic data sets feasible.