Diatom Lensless Imaging Using Laser Scattering and Deep Learning

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.

Monitoring diatoms is important in understanding the health of the marine environment. This study documents the use of lensless sensing to image samples of diatoms and quantify their movement.