Physics-enhanced neural network for phase retrieval from two diffraction patterns

doi:10.1364/OE.469080

Physics-enhanced neural network for phase retrieval from two diffraction patterns

2022-08-22

18

OA

AI

DOI：10.1364/OE.469080

Original

Original request for help

Save

Abstract

En 中文

In this work, we propose a physics-enhanced two-to-one Y-neural network (two inputs and one output) for phase retrieval of complex wavefronts from two diffraction patterns. The learnable parameters of the Y-net are optimized by minimizing a hybrid loss function, which evaluates the root-mean-square error and normalized Pearson correlated coefficient on the two diffraction planes. An angular spectrum method network is designed for self-supervised training on the Y-net. Amplitudes and phases of wavefronts diffracted by a USAF-1951 resolution target, a phase grating of 200 lp/mm, and a skeletal muscle cell were retrieved using a Y-net with 100 learning iterations. Fast reconstructions could be realized without constraints or a priori knowledge of the samples. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Keywords:

TRANSPORT

RECONSTRUCTION

PROPAGATION

Physics-enhanced neural network for phase retrieval from two diffraction patterns

Abstract

Journal

Organization