Application of a Deep Neural Network to Phase Retrieval in Inverse Medium Scattering Problems

Abstract

We address the inverse medium scattering problem with phaseless data motivated by nondestructive testing for optical fibers. As the phase information of the data is unknown, this problem may be regarded as a standard phase retrieval problem that consists of identifying the phase from the amplitude of data and the structure of the related operator. This problem has been studied intensively due to its wide applications in physics and engineering. However, the uniqueness of the inverse problem with phaseless data is still open and the problem itself is severely ill-posed. In this work, we construct a model to approximate the solution operator in finite-dimensional spaces by a deep neural network assuming that the refractive index is radially symmetric. We are then able to recover the refractive index from the phaseless data. Numerical experiments are presented to illustrate the effectiveness of the proposed model.

Measurement of Small-Slope Free-Form Optical Surfaces with the Modified Phase Retrieval

“…In this paper, we present a modified gradient search algorithm to solve the phase recovery problem [22,23]. Let g m,k , θ m,k , G m,k , ϕ m,k be the estimated values of f , δ, F, ψ when the mth images iterate k times, g k represents the combined estimate value with every g m,k to f when iterated k times, which is…”