Dynamic 2D implementation of 3D diffractive optics

Author(s):

Haiyan Wang and Rafael Piestun

Abstract:

“Volumetric computer-generated diffractive optics offer advantages over planar 2D implementations, including the generation of space-variant functions and the multiplexing of information in space or frequency domains. Unfortunately, despite remarkable progress, fabrication of high volumetric space-bandwidth micro- and nanostructures
is still in its infancy. Furthermore, existing 3D diffractive optics implementations are static while programmable volumetric spatial light modulators (SLMs) are still years or decades away. In order to address these shortcomings, we propose the implementation of volumetric diffractive optics equivalent functionality via cascaded
planar elements. To illustrate the principle, we design 3D diffractive optics and implement a two-layer continuous phase-only design on a single SLM with a folded setup. The system provides dynamic and efficient multiplexing capability. Numerical and experimental results show this approach improves system performance such as diffraction
efficiency, spatial/spectral selectivity, and number of multiplexing functions relative to 2D devices while providing dynamic large space-bandwidth relative to current static volume diffractive optics. The limitations and capabilities of dynamic 3D diffractive optics are discussed.”

Link to Publications Page

Publication: Optica

Issue/Year/DOI: Optica Volume 5, Issues 10
DOI: doi.org/10.1364/OPTICA.5.001220

Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media

Author(s):

Yunzhe Li and Yujia Xue and Lei Tian

Abstract:

“Imaging through scattering is an important yet challenging problem. Tremendous progress has been made by exploiting the deterministic input–output “transmission matrix” for a fixed medium. However, this “one-to-one” mapping is highly susceptible to speckle decorrelations – small perturbations to the scattering medium lead to model errors and severe degradation of the imaging performance. Our goal here is to develop a new framework that is highly scalable to both medium perturbations and measurement requirement. To do so, we propose a statistical “one-to-all” deep learning (DL) technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show for the first time, to the best of our knowledge, that the trained CNN is able to generalize and make high-quality object predictions through an entirely different set of diffusers of the same class. Our work paves the way to a highly scalable DL approach for imaging through scattering media.”

Link to Publications Page

Publication: Optica

Issue/Year/DOI: Optica Volume 5, Issue 10 pp. 1181-1190 (2018)
DOI: 10.1364/OPTICA.5.001181