Machine learning for improved size estimation of complex marine particles from noisy holographic images

Machine learning for improved size estimation of complex marine particles from noisy holographic images

Size estimation of particles and plankton is key to understanding energy flows in the marine ecosystem. A useful tool to determine particle and plankton size - besides abundance and taxonomy - is in situ imaging, with digital holography being particularly useful for micro-scale (e.g., 25 – 2,500 µm)...

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Bibliographic Details
Main Authors: Zonghua Liu, Marika Takeuchi, Yéssica Contreras, Thangavel Thevar, Alex Nimmo-Smith, John Watson, Sarah L. C. Giering
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Marine Science
Subjects:
subsea digital holography
hologram processing
machine learning
size estimation
particle size distributions
Online Access:https://www.frontiersin.org/articles/10.3389/fmars.2025.1587939/full
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https://www.frontiersin.org/articles/10.3389/fmars.2025.1587939/full

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