Real-time Deep THz Imaging

A physical neural network for THz imaging employs hybrid optical-digital signal dimension transformation to address hardware limitations, achieving high-quality, diffraction-free, real-time imaging at 2 frames per second with an expandable field of view.

Authors

Shao‐Hsuan Wu
Hanyu Liang (ORCID: https://orcid.org/0000-0002-5893-8750)
Shang-Hua Yang
Seyed Mostafa Latifi (ORCID: https://orcid.org/0000-0002-3273-7707)

Publication Details

Published: 2025-01-01
DOI: https://doi.org/10.1364/cleo_at.2025.aa113_4
Primary Topic: Neural Networks and Reservoir Computing
Type: article
Field-Weighted Citation Impact: 0.00

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article

Real-time Deep THz Imaging

Shao‐Hsuan Wu, Hanyu Liang, Shang-Hua Yang, Seyed Mostafa Latifi

Jan 1, 2025

Neural Networks and Reservoir Computing

article

Real-time Deep THz Imaging

Shao‐Hsuan Wu, Hanyu Liang, Shang-Hua Yang, Seyed Mostafa Latifi

January 1, 2025article en

Abstract

Openalex Percentile: Top 87%

Neural Networks and Reservoir Computing

DOI Source OpenAlex

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Real-time Deep THz Imaging — Shao‐Hsuan Wu, Hanyu Liang, et al. · (2025) | TGRS Research Map | TGRS