Diatom lensless imaging using laser scattering and deep learning
Diatom lensless imaging using laser scattering and deep learning
We present a novel approach for imaging diatoms using lensless imaging and deep learning. We used a laser beam to scatter off samples of diatomaceous earth (diatoms) and then recorded and transformed the scattered light into microscopy images of the diatoms. The predicted microscopy images gave an average SSIM of 0.98 and an average RMSE of 3.26 as compared to the experimental data. We also demonstrate the capability of determining the velocity and angle of movement of the diatoms from their scattering patterns as they were translated through the laser beam. This work shows the potential for imaging and identifying the movement of diatoms and other microsized organisms in situ within the marine environment. Implementing such a method for real-time image acquisition and analysis could enhance environmental management, including improving the early detection of harmful algal blooms.
deep learning, diatoms, lasers, lensless sensing, scattering
1814–1820
Mills, Ben
05f1886e-96ef-420f-b856-4115f4ab36d0
Zervas, Michalis N.
1840a474-dd50-4a55-ab74-6f086aa3f701
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
24 March 2025
Mills, Ben
05f1886e-96ef-420f-b856-4115f4ab36d0
Zervas, Michalis N.
1840a474-dd50-4a55-ab74-6f086aa3f701
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
Mills, Ben, Zervas, Michalis N. and Grant-Jacob, James A.
(2025)
Diatom lensless imaging using laser scattering and deep learning.
ACS ES&T Water, 5 (4), .
(doi:10.1021/acsestwater.4c01186).
Abstract
We present a novel approach for imaging diatoms using lensless imaging and deep learning. We used a laser beam to scatter off samples of diatomaceous earth (diatoms) and then recorded and transformed the scattered light into microscopy images of the diatoms. The predicted microscopy images gave an average SSIM of 0.98 and an average RMSE of 3.26 as compared to the experimental data. We also demonstrate the capability of determining the velocity and angle of movement of the diatoms from their scattering patterns as they were translated through the laser beam. This work shows the potential for imaging and identifying the movement of diatoms and other microsized organisms in situ within the marine environment. Implementing such a method for real-time image acquisition and analysis could enhance environmental management, including improving the early detection of harmful algal blooms.
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Diatoms_10032025_Accepted_Changes
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mills-et-al-2025-diatom-lensless-imaging-using-laser-scattering-and-deep-learning
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More information
Accepted/In Press date: 14 March 2025
Published date: 24 March 2025
Keywords:
deep learning, diatoms, lasers, lensless sensing, scattering
Identifiers
Local EPrints ID: 500107
URI: http://eprints.soton.ac.uk/id/eprint/500107
ISSN: 2690-0637
PURE UUID: 155768c3-26bd-467f-9e61-cf850f0e7042
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Date deposited: 15 Apr 2025 17:00
Last modified: 22 Aug 2025 02:03
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Contributors
Author:
Ben Mills
Author:
Michalis N. Zervas
Author:
James A. Grant-Jacob
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