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 cos...
Published in: | Limnology and Oceanography: Methods |
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Online Access: | http://dx.doi.org/10.1002/lom3.10438 https://onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10438 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/lom3.10438 https://aslopubs.onlinelibrary.wiley.com/doi/am-pdf/10.1002/lom3.10438 https://aslopubs.onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10438 |
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crwiley:10.1002/lom3.10438 2024-06-02T08:12:16+00:00 Computationally efficient processing of in situ underwater digital holograms Cotter, Emma Fischell, Erin Lavery, Andone Center for Sponsored Coastal Ocean Research National Science Foundation 2021 http://dx.doi.org/10.1002/lom3.10438 https://onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10438 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/lom3.10438 https://aslopubs.onlinelibrary.wiley.com/doi/am-pdf/10.1002/lom3.10438 https://aslopubs.onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10438 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#am http://onlinelibrary.wiley.com/termsAndConditions#vor Limnology and Oceanography: Methods volume 19, issue 7, page 476-487 ISSN 1541-5856 1541-5856 journal-article 2021 crwiley https://doi.org/10.1002/lom3.10438 2024-05-03T11:01:35Z 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 mm 2 , 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. Article in Journal/Newspaper Northwest Atlantic Copepods Wiley Online Library Limnology and Oceanography: Methods 19 7 476 487 |
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English |
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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 mm 2 , 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. |
author2 |
Center for Sponsored Coastal Ocean Research National Science Foundation |
format |
Article in Journal/Newspaper |
author |
Cotter, Emma Fischell, Erin Lavery, Andone |
spellingShingle |
Cotter, Emma Fischell, Erin Lavery, Andone Computationally efficient processing of in situ underwater digital holograms |
author_facet |
Cotter, Emma Fischell, Erin Lavery, Andone |
author_sort |
Cotter, Emma |
title |
Computationally efficient processing of in situ underwater digital holograms |
title_short |
Computationally efficient processing of in situ underwater digital holograms |
title_full |
Computationally efficient processing of in situ underwater digital holograms |
title_fullStr |
Computationally efficient processing of in situ underwater digital holograms |
title_full_unstemmed |
Computationally efficient processing of in situ underwater digital holograms |
title_sort |
computationally efficient processing of in situ underwater digital holograms |
publisher |
Wiley |
publishDate |
2021 |
url |
http://dx.doi.org/10.1002/lom3.10438 https://onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10438 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/lom3.10438 https://aslopubs.onlinelibrary.wiley.com/doi/am-pdf/10.1002/lom3.10438 https://aslopubs.onlinelibrary.wiley.com/doi/pdf/10.1002/lom3.10438 |
genre |
Northwest Atlantic Copepods |
genre_facet |
Northwest Atlantic Copepods |
op_source |
Limnology and Oceanography: Methods volume 19, issue 7, page 476-487 ISSN 1541-5856 1541-5856 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#am http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1002/lom3.10438 |
container_title |
Limnology and Oceanography: Methods |
container_volume |
19 |
container_issue |
7 |
container_start_page |
476 |
op_container_end_page |
487 |
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1800758651716108288 |