Task-Driven Learning Downsampling Network Based Phase-Resolved Wave Fields Reconstruction with Remote Optical Observations

We develop a phase-resolved wave field reconstruction method by the learning-based downsampling network for processing large amounts of inhomogeneous data from non-contact wave optical observations. The Waves Acquisition Stereo System (WASS) extracts dense point clouds from ocean wave snapshots. We couple learning-based downsampling networks with the phase-resolved wave reconstruction algorithm, and the training task is to improve the wave reconstruction completeness ratio $C_R$. The algorithm first achieves initial convergence and task-optimized performance on numerical ocean waves built by the linear wave theory model. Results show that the trained sampling network can lead to a more uniform spatial distribution of sampling points and improve $C_R$ at the observed edge regions far from the optical camera. Finally, we apply our algorithm to a natural ocean wave dataset. The average completeness ratio is improved over 30% at low sampling ratios ($S_R\in [2^{-9},2^{-7}]$) compared to the traditional FPS method and Random sampling method. Moreover, the relative residual between the final reconstructed wave and the natural wave is less than 15%, which provides an efficient tool for wave reconstruction in ocean engineering.