• Technical Conference: 

    09 – 14 May 2021

  • Exhibition: 

    10 – 14 May 2021

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FM3L

Scattering and Imaging

Presider: Rajesh Menon, University of Utah

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Presentations

INtensity Statistics: a Fingerprint for Waves Evolution in the Diffusion Regime (FM3L.1)
Presenter: Ruitao Wu, CREOL

We demonstrate that intensity statistics is nonstationary in diffusive regimes of waves in reflection from random media. A statistical model based on recurrent scattering and near field coupling is proposed and confirmed experimentally.

Authors:Ruitao Wu, CREOL / Aristide Dogariu, CREOL

  Paper

Anderson Localization of Hybrid Quasiparticles: Anomalous Transmission due to Necklace States (FM3L.2)
Presenter: Sandip Mondal, Tata Institute of Fundamental Research

We report a novel transport regime under Anderson-localizing disorder conditions, wherein hybrid photon-plasmon quasiparticles undergo strong transmission. The effect occurs due to creation of necklace states enabled by closely-spaced eigenstates that exchange energy due to inherent non-Hermiticity.

Authors:Sandip Mondal, Tata Institute of Fundamental Research / M. Balasubrahmaniyam, Tata Institute of Fundamental Research / Hitaisini Sahoo, Tata Institute of Fundamental Research / Meghan Patankar, Tata Institute of Fundamental Research / R. Vijayaraghavan, Tata Institute of Fundamental Research / Sushil Mujumdar, Tata Institute of Fundamental Research

  Paper

Light Scattering of Random Close Packed Nanorods (FM3L.3)
Presenter: Mutasem ODEH, UC Berkeley

In this work, we investigate the scattering behavior of nanorods that are randomly packed at various densities and aspect ratios. We show that the maximum packing density, maximum scattering density, and the percolation threshold are all tightly related to Onsager excluded-area principle.

Authors:Mutasem ODEH, UC Berkeley / Matthieu Dupre, UCSD / Kevin Kim, UCSD / Boubacar Kanté, UC Berkeley

  Paper

Stabilized Depth Cell Imaging Through Disordered Fiber System With Semi-Supervised Learning Algorithm (FM3L.4)
Presenter: Xiaowen Hu, University of Central Florida

With ground truths only at 0 imaging depth, we reconstruct high-quality cell images through disordered optical fiber system up to 3mm imaging depth by tracing the state of the system using cycle-consistent adversarial networks.

Authors:Xiaowen Hu, University of Central Florida / Jian Zhao, University of Central Florida / Jose Antonio-Lopez, University of Central Florida / Youyou Cheng, University of Central Florida / Rodrigo Amezcua Correa, University of Central Florida / Axel Schülzgen, University of Central Florida

  Paper

Single-Shot Imaging Through Scattering Media Under Strong Background Interferences (FM3L.5)
Presenter: shunfu he, Xidian University

We report and demonstrate experimentally an approach to retrieving the object from a single-shot speckle pattern under a strong background light interference. This approach provides a practical solution to natural scene scattering imaging.

Authors:shunfu he, Xidian University / Wei Li, Xidian University / Teli Xi, Xidian University / Yangfan Sun, Xidian University / Jietao Liu, Xidian University / Xiaopeng Shao, Xidian University

  Paper

Super-Resolution Sensing With a Randomly Scattering Analyzer (FM3L.6)
Presenter: Justin Patel, Purdue University

A randomly scattering analyzer is presented as means to access super-resolution spatial sensing information associated with subwavelength
motion of a coherent incident field or remote object and illustrated using cross-correlations of normalized laser speckle patterns.

Authors:Justin Patel, Purdue University / Qiaoen Luo, Purdue University / Kevin Webb, Purdue University

  Paper

Misalignment Tolerant Diffractive Optical Networks (FM3L.7)
Presenter: Deniz Mengu, University of California, Los Angeles

Design of diffractive optical networks that are resilient against physical misalignments is reported. The success of this design framework is also experimentally demonstrated using 3D printed diffractive networks that operate at THz wavelengths.

Authors:Deniz Mengu, University of California, Los Angeles / Yifan Zhao, University of California, Los Angeles / Nezih Yardimci, University of California, Los Angeles / Yair Rivenson, University of California, Los Angeles / Mona Jarrahi, University of California, Los Angeles / Aydogan Ozcan, University of California, Los Angeles

  Paper

Single-Pixel Machine Vision Using Spectral Encoding Through Diffractive Optical Networks (FM3L.8)
Presenter: Jingxi Li, University of California, Los Angeles

We present and experimentally demonstrate a deep learning-driven machine-vision framework that trains diffractive surfaces to encode the spatial information objects into the output power spectrum for all-optical image classification using a single-pixel spectroscopic detector.

Authors:Jingxi Li, University of California, Los Angeles / Deniz Mengu, University of California, Los Angeles / Nezih Yardimci, University of California, Los Angeles / Yi Luo, University of California, Los Angeles / Xurong Li, University of California, Los Angeles / Muhammed Veli, University of California, Los Angeles / Yair Rivenson, University of California, Los Angeles / Mona Jarrahi, University of California, Los Angeles / Aydogan Ozcan, University of California, Los Angeles

  Paper