Learning-Based THz Multi-Layer Imaging With Model-Based Masks

被引：0

作者：

Wang, P. ^{[1
]}

Koike-Akino, T. ^{[1
]}

Boufounos, P. ^{[1
]}

Tsujita, W. ^{[1
]}

Yamashita, G. ^{[2
]}

Fukuta, T. ^{[2
]}

Nakajima, M. ^{[3
]}

机构：

[1] Mitsubishi Elect Res Labs, Cambridge, MA 02139 USA

[2] Mitsubishi Elect Corp Adv Technology R&D Ctr, Amagasaki, Hyogo 6618661, Japan

[3] Osaka Univ, Inst Laser Engn, Osaka 5650871, Japan

来源：

2023 48TH INTERNATIONAL CONFERENCE ON INFRARED, MILLIMETER, AND TERAHERTZ WAVES, IRMMW-THZ | 2023年

关键词：

D O I：

10.1109/IRMMW-THz57677.2023.10299043

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper demonstrates a learning-based THz multi-layer pixel identification for non-destructive inspection. Specifically, we introduce a recurrent neural network that sequentially learns features from THz spectrogram segments with masks from model-based sparse deconvolution. Initial performance evaluation on a three-layer sample with contents on all surfaces confirms the effectiveness of the proposed method.

引用

页数：2

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