Phase Retrieval for Fourier THz Imaging with Physics-Informed Deep Learning

Xiang, Mingjun; Wang, Lingxiao; Yuan, Hui; Zhou, Kai; Roskos, Hartmut G.

doi:10.1109/irmmw-thz50927.2022.9895691

Cited by 2 publications

(2 citation statements)

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“…The reconstruction of THz images based on deep learning for superior image quality through high and super resolution image reconstruction was also explored for; 3D THz image reconstruction [137], THz CT 3D image reconstruction [138]- [140] and 3D THz aperture radar imaging [141], [142]. THz coded aperture radar imaging.…”

Section: High and Super Resolution Thz Image Reconstructionmentioning

confidence: 99%

“…The right-hand side of the architecture is the layout of the adaptive network (Na). The simulation and tested data acquired using a frequency modulated continuous wave (FMCW) real aperture scanner demonstrated effectiveness and superiority of their method quantitatively and qualitatively[136].The reconstruction of THz images based on deep learning for superior image quality through high and super resolution image reconstruction was also explored for; 3D THz image reconstruction[137], THz CT 3D image reconstruction[138]-[140] and 3D THz aperture radar imaging[141],[142]. THz coded aperture radar imaging.…”

mentioning

confidence: 99%

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Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Gezimati,

Singh

2024

IEEE Access

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Following the recent progress in the development of Terahertz (THz) generation and detection, THz technology is being widely used to characterize test sample properties in various applications including nondestructive testing, security inspection and medical applications. In this paper, we have presented a broad review of the recent usage of artificial intelligence (AI) particularly, deep learning techniques in various THz sensing, imaging, and spectroscopic applications with emphasis on their implementation for medical imaging of cancerous cells. Initially, the fundamentals principles and techniques for THz generation and detection, imaging and spectroscopy are introduced. Subsequently, a brief overview of AI -machine learning and deep learning techniques is summarized, and their performance is compared. Further, the usage of deep learning algorithms in various THz applications is reported, with focus on metamaterials design and classification, detection, reconstruction, segmentation, parameter extraction and denoising tasks. Moreover, we also report the metrics used to evaluate the performance of deep learning models and finally, the existing research challenges in the application of deep learning in THz cancer imaging applications are identified and possible solutions are suggested through emerging trends. With the continuous increase of acquired THz data -sensing, spectral and imaging, artificial intelligence has emerged as a dominant paradigm for embedded data extraction, understanding, perception, decision making and analysis. Towards this end, the integration of state-of-the-art machine learning techniques such as deep learning with THz applications enable detailed computational and theoretical analysis for better validation and verification than modelling techniques that precede the era of machine learning. The study will facilitate the large-scale clinical applications of deep learning enabled THz imaging systems for the development of smart and connected next generation healthcare systems as well as provide a roadmap for future research direction.

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