Randomized probe imaging through deep k-learning
Name
oe-30-2-2247.pdf
Description
Published version
Size
8 MB
Format
Adobe PDF
Checksum (MD5)
3d01dbea70550c772c78796ee802ca9c
Author(s)
Guo, Zhen
Levitan, Abraham
Barbastathis, George
Comin, Riccardo
Date Issued
January 17, 2022
Journal
Optics Express
Publisher
The Optical Society
Citation
Guo, Zhen, Levitan, Abraham, Barbastathis, George and Comin, Riccardo. 2022. "Randomized probe imaging through deep k-learning." Optics Express, 30 (2).
Version
Final published version
Abstract
Randomized probe imaging (RPI) is a single-frame diffractive imaging method that uses highly randomized light to reconstruct the spatial features of a scattering object. The reconstruction process, known as phase retrieval, aims to recover a unique solution for the object without measuring the far-field phase information. Typically, reconstruction is done via time-consuming iterative algorithms. In this work, we propose a fast and efficient deep learning based method to reconstruct phase objects from RPI data. The method, which we call deep k-learning, applies the physical propagation operator to generate an approximation of the object as an input to the neural network. This way, the network no longer needs to parametrize the far-field diffraction physics, dramatically improving the results. Deep k-learning is shown to be computationally efficient and robust to Poisson noise. The advantages provided by our method may enable the analysis of far larger datasets in photon starved conditions, with important applications to the study of dynamic phenomena in physical science and biological engineering.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
Singapore-MIT Alliance in Research and Technology (SMART)
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1364/oe.445498
