Compressive ultrafast pulse measurement via time-domain single-pixel imaging

Abstract

In contrast to imaging using position-resolving cameras, single-pixel imaging uses a bucket detector along with spatially structured illumination to compressively recover images. This emerging imaging technique is a promising candidate for a broad range of applications due to the high signal-to-noise ratio (SNR) and sensitivity, and applicability in a wide range of frequency bands. Here, inspired by single-pixel imaging in the spatial domain, we demonstrate a time-domain single-pixel imaging (TSPI) system that covers frequency bands including both terahertz (THz) and near-infrared (NIR) regions. By implementing a programmable temporal fan-out gate based on a digital micromirror device, we can deterministically prepare temporally structured pulses with a temporal sampling size down to 16.00 ± 0.01 f s . By inheriting the advantages of detection efficiency and sensitivity from spatial single-pixel imaging, TSPI enables the recovery of a 5 fJ THz pulse and two NIR pulses with over 97 % fidelity via compressive sensing. We demonstrate that the TSPI is robust against temporal distortions in the probe pulse train as well. As a direct application, we apply TSPI to machine-learning-aided THz spectroscopy and demonstrate a high sample identification accuracy (97.5%) even under low SNRs (SNR ∼ 10 ).

Document Details

Document Type
Pub Defense Publication
Publication Date
Aug 30, 2021
Source ID
10.1364/optica.431455

Entities

People

  • Boris Braverman
  • Jianming Dai
  • Jiapeng Zhao
  • Robert W. Boyd
  • Xi-Cheng Zhang

Organizations

  • National Natural Science Foundation of China
  • Office of Naval Research
  • Tianjin University
  • University of Ottawa
  • University of Rochester

Tags

Fields of Study

  • Physics

Readers

  • Image Processing and Computer Vision.
  • Optical Physics and Photonics.
  • Quantum Dot Semiconductor Device Photonics and Graphene Optoelectronic Materials and THz Physics.

Technology Areas

  • AI & ML