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In-line holographic droplet imaging: accelerated classification with convolutional neural networks and quantitative experimental validation.

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Title: In-line holographic droplet imaging: accelerated classification with convolutional neural networks and quantitative experimental validation.
Authors: Thiede, Birte1,2 (AUTHOR), Schlenczek, Oliver2 (AUTHOR), Stieger, Katja1,2 (AUTHOR), Ecker, Alexander2,3 (AUTHOR), Bodenschatz, Eberhard1,2,4 (AUTHOR), Bagheri, Gholamhossein2 (AUTHOR) gholamhossein.bagheri@ds.mpg.de
Source: Atmospheric Measurement Techniques. 2025, Vol. 18 Issue 21, p6291-6314. 24p.
Database: Academic Search Ultimate
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  Data: In-line holographic droplet imaging: accelerated classification with convolutional neural networks and quantitative experimental validation.
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  Data: <searchLink fieldCode="JN" term="%22Atmospheric+Measurement+Techniques%22">Atmospheric Measurement Techniques</searchLink>. 2025, Vol. 18 Issue 21, p6291-6314. 24p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=189395600
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        Value: 10.5194/amt-18-6291-2025
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      – Code: eng
        Text: English
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      – TitleFull: In-line holographic droplet imaging: accelerated classification with convolutional neural networks and quantitative experimental validation.
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            – D: 01
              M: 11
              Text: 2025
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              Y: 2025
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