A two-stage neural network recovering phase from a single-frame phase-shifted hologram

Abstract

Quantitative phase imaging and measurement of surface topography and fluid dynamics for objects, especially for moving objects, is critical in various fields. Phase-shifting digital holography, as a highly accurate phase measurement technology applied for moving objects, is limited by some aspects, such as dynamic phase measurement, accuracy of phase shift and temporal phase sensitivity. In this study, we proposed a two-stage neural network (VY-Net) for one shot phase recovery. This Y-Net generates two holograms with specific phase shifts from a single-frame phase shifted hologram, then V-Net recovering the phase with the three holograms input. Simulation results prove that the proposed method can provide an alternative approach for systems of phase-shifting digital holography based on common-path configuration to realize rapid phase-shifted holograms acquisition and accurate phase measurement.