Phase reconstruction and singularity recovery of submicron particles in far-field phase space data using deep learning networks

Abstract

Phase images often contain more comprehensive information compared to amplitude images. The precise visualization of phase images generated from light scattering by submicron particles is paramount for investigating the underlying scattering mechanisms and exploring applications centered around these submicron particles. This study showcases the efficacy of neural networks in acquiring the capacity to conduct phase reconstruction and recover singularities through appropriate training. Our deep learning-based approach introduces an entirely novel framework for phase recovery, accomplished by learning the distribution of scattered light fields from submicron particles, as obtained through a polarized indirect microscopic imaging (PIMI) system, within a custom Poincaré sphere (phase space). We validate this method by successfully reconstructing phase images of polystyrene (PS) balls with varying radii, and comparing diverse network structures and data flows in the process. Furthermore, we substantiate our experimental results by corroborating them with finite difference time domain (FDTD) simulations. These findings underscore the remarkable regularity within the distribution of phase space data, opening up new avenues for advancing the study of nanostructures.