Fast and High-Quality 3-D Terahertz Super-Resolution Imaging Using Lightweight SR-CNN

Fan, Lei; Zeng, Yang; Yang, Qi; Wang, Hongqiang; Deng, Bin

doi:10.3390/rs13193800

Cited by 9 publications

(5 citation statements)

References 41 publications

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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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Section: High and Super Resolution Thz Image Reconstructionmentioning

confidence: 99%

mentioning

confidence: 99%

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

Gezimati,

Singh

2024

IEEE Access

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“…The developments in system-on-chip complementary metal oxide semiconductor (CMOS) radio frequency integrated circuits (RFIC) have resulted in the emergence of cost-effective frequency modulated continuous wave (FMCW) millimetre wave radars for imaging applications. The radar imaging systems include multiple input multiple output SAR (MIMO SAR), interferometric SAR (InSAR) and tomographic SAR [22]- [26]. In [8], a THz near-field imaging system based on SAR has been developed for precise, high resolution NDT of packaged aluminium etched antenna arrays and for flexible gold electrode array surface imaging.…”

Section: B Related Workmentioning

confidence: 99%

“…where 𝑤 and 𝑤 are the azimuth and range envelope respectively, 𝑇 is synthetic aperture time, 𝑓 is the range frequency variable, 𝐵 denotes the transmitted signal bandwidth and ∆𝑅 is the differential range. The full derivation of the PFA image reconstruction process has been derived in [22,23].…”

Section: G Polar Format Algorithmmentioning

confidence: 99%

Curved Synthetic Aperture Radar for Near-Field Terahertz Imaging

Gezimati

Singh

2023

IEEE Photonics J.

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High resolution imaging technique will play an essential role in the future smart imaging applications to provide potential solutions for accurate detection, monitoring and classification of the target. The potential of synthetic aperture radar (SAR) systems to acquire high resolution images has opened new frontiers and led to its exploration in new applications. The conventional microwave radar imaging suffers limited range resolution in the sub-millimeter wave range and optical imaging methods are constrained by the diffraction limit. In the terahertz (THz) regime of the spectrum, the SAR systems could be operated in all the weather, all the time and are capable of penetrating through clouds, smoke, dust etc. to achieve high resolution images beyond the diffraction limit. The curved SAR presents a SAR system created by circularly encircling the target scene. The Circular SAR and Spiral SAR which are based on a circular and cylindrical spiral scanning trajectory, respectively are the scanning methods used for curved SAR. In this paper, we have explored a new toolbox that enables the rapid development of near-field THz-SAR imaging systems and generation of large near-field THz imaging scenarios with the goal to support and facilitate data driven research such as deep learning in the THz community through synthetically generated SAR images as well as its potential for THz based high resolution and 2D/3D near-field imaging application. Initially, the potential advantages of THz-SAR over Microwave SAR and optical imaging methods are presented. Further, a frequency modulated continuous wave (FMCW) radar testbed is simulated in multi-input multi-output (MIMO) configuration for the target frequency band. Moreover, the circular and cylindrical SAR scanning methods are explored for near-field imaging of point targets to obtain 2D and 3D THz-SAR images, respectively. Also, the radar images are reconstructed using the Back Propagation and Polar Formatting Algorithms, focusing on short range applications like indoor environments. Finally, the SAR performance evaluation metrics are reported and a roadmap for future work is presented.

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“…Deeper networks can extract more information for picture reconstruction, but network overfitting makes training difficult. After that, Fan et al [ 21 ] proposed a lightweight SRCNN method to improve the image quality and to meet the demand of small datasets. In 2021, Su et al [ 22 ] suggested a novel subspace-and-attention-guided restoration network (SARNet), which adopted attention guidance to fuse spatio-spectral features of amplitude and phase.…”

Section: Introductionmentioning

confidence: 99%

Super-Resolution Reconstruction of Terahertz Images Based on Residual Generative Adversarial Network with Enhanced Attention

Hou

Cha

et al. 2023

Entropy

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Terahertz (THz) waves are widely used in the field of non-destructive testing (NDT). However, terahertz images have issues with limited spatial resolution and fuzzy features because of the constraints of the imaging equipment and imaging algorithms. To solve these problems, we propose a residual generative adversarial network based on enhanced attention (EA), which aims to pay more attention to the reconstruction of textures and details while not influencing the image outlines. Our method successfully recovers detailed texture information from low-resolution images, as demonstrated by experiments on the benchmark datasets Set5 and Set14. To use the network to improve the resolution of terahertz images, we create an image degradation algorithm and a database of terahertz degradation images. Finally, the real reconstruction of terahertz images confirms the effectiveness of our method.

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Fast and High-Quality 3-D Terahertz Super-Resolution Imaging Using Lightweight SR-CNN

Cited by 9 publications

References 41 publications

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

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

Curved Synthetic Aperture Radar for Near-Field Terahertz Imaging

Super-Resolution Reconstruction of Terahertz Images Based on Residual Generative Adversarial Network with Enhanced Attention

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