Work

Diatom Lensless Imaging Using Laser Scattering and Deep Learning

Year: 2025

Type: article

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.

Source: ACS ES&T Water

DOI: 10.1021/acsestwater.4c01186

Authors: Ben Mills, Michalis N. Zervas, James A. Grant-Jacob

Institution: University of Southampton

Language: English

Topic: Digital Holography and Microscopy

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,500

Funder: Engineering and Physical Sciences Research Council