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.

中文翻译：

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原位水下数字全息图的计算高效处理

水下数字在线全息可以提供高分辨率的海洋粒子原位图像，并提供许多优于其他测量方法的优势。然而，全息图的处理需要计算上昂贵的重建和处理，并且计算成本随着成像体积的大小而增加。在这项工作中，开发了一个处理管道来从全息图中提取目标，其中目标分布相对稀疏，而无需重建完整的全息图。这是由于希望使用大容量全息相机从西北大西洋收集的数据集（> 300,000 个全息图）中有效提取浮游生物丰度的定量估计。首先，使用迁移学习方法选择具有可检测目标的全息图。这是至关重要的，因为全息图的一个子集受到光学湍流的影响，这会掩盖目标检测。然后，在全息图中检测目标衍射图案。最后，仅使用检测到的衍射图案周围的全息图的小区域来重建和聚焦目标。采用搜索算法选择重建距离，将 1 mm 聚焦精度所需的重建次数从 1000 次减少到 31 次。与全重建技术相比，该方法可检测 99% 大于 0.1 mm 的粒子^{如图 2 所示}，一个尺寸等级包括大多数桡足类动物和较大的海洋雪颗粒，并且这些目标的 85% 足够集中用于分类。这种方法需要计算完整重建所需的处理时间的 1%，从而使处理长时间序列、大成像体积全息数据集变得可行。