Wave (from) Polarized Light Learning (WPLL) method: high resolution spatio-temporal measurements of water surface waves in laboratory setups

Barak Fishbain, Noam Ginio

Abstract:

Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are essential for
scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer
from limited wavenumber/frequency response. To address this challenge, we propose the Wave (from) Polarized Light
Learning (WPLL), a learning based remote sensing method for laboratory implementation, capable of inferring surface
elevation and slope maps in high resolution. The method uses the polarization properties of the light reflected from the water
surface. The WPLL uses a deep neural network (DNN) model that approximates the water surface slopes from the polarized
light intensities. Once trained on simple monochromatic wave trains, the WPLL is capable of producing high-resolution and
accurate reconstruction of the 2D water surface slopes and elevation in a variety of irregular wave fields. The method’s
robustness is demonstrated by showcasing its high wavenumber/frequency response, its ability to reconstruct wave fields
propagating in arbitrary angles relative to the camera optical axis, and its computational efficiency. This developed
methodology is an accurate and cost-effective near-real time remote sensing tool for laboratory water surface waves
measurements, setting the path for upscaling to open sea application for research, monitoring, and short-time forecasting.