Learning-Based Split Unfolding Framework for 3-D mmW Radar Sparse Imaging

Wei, Shunjun; Zhou, Zichen; Wang, Mou; Zhang, Hao; Shi, Jun; Fan, Ling

doi:10.1109/tgrs.2022.3181174

Cited by 8 publications

(1 citation statement)

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“…CS algorithm are used in the mmW SAR imaging [25,26]. Besides, CS for 3D mmW imaging is proposed in the studies [27][28][29][30] The other popular reconstruction method is BPA. BPA is first proposed for medical imaging systems [31,32].…”

Section: Related Workmentioning

confidence: 99%

Conical Differential Range Based Back-Projection Algorithm for Concealed Object Detection with Three-Dimensional mmW Imaging

Duysak¹,

Yiğit²,

Seyfi³

2022

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Millimeter wave(mmW) imaging has spread to a wide range of applications in the last quarter. One of the most important research areas of mmW is three-dimensional (3D) imaging systems. In this study, conical differential range-based back-projection (BP) algorithm is proposed for three-dimensional mmW imaging. In the algorithm, the differential range is created using points inside a conical volume, thus the number of interpolation points is considerably reduced. The performance of the algorithm is demonstrated by simulation and experimental studies. Cylindrical scanning is carried out by means of the experimental setup. Experiments are carried out at frequencies of 26.5-40 GHz. The traditional BP algorithm (BPA) and the proposed algorithm are used to reconstruct the images. With the proposed method, it is observed that ISLR for the point target increased by about 5 dB compared to the traditional method. Moreover, the computational complexity is reduced by up to 10 times, depending on the imaging area. Thanks to the proposed method, the image of the concealed weapon under the cloth in an experimental study is more clearly focused compared to the traditional method. Therefore, it can provide images that give more accurate results for applications such as automatic target detection methods.

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Section: Related Workmentioning

confidence: 99%

Conical Differential Range Based Back-Projection Algorithm for Concealed Object Detection with Three-Dimensional mmW Imaging

Duysak¹,

Yiğit²,

Seyfi³

2022

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Deep Learning Infrared Holography with Transformer for Crystal Material Characterization

Li,

Huang,

Sun

et al. 2024

Crystal Growth & Design

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This investigation presents a novel approach for the nondestructive, and real-time analysis of crystalline structures, including transition metal dichalcogenides renowned for their optoelectronic capabilities. The methodology employs a synergistic blend of infrared digital holography and deep learning, utilizing an in-line system and Transformer-based deep learning algorithms, to provide detail in the material microstructure. The article investigates the effects of different parameters on reproduction fidelity, with a particular focus on phase accuracy. A holography-guided training strategy is proposed to enhance the framework's performance. By demonstration applications such as evaluating the dielectric characteristics of ReS 2 , detecting material thickness layers in MoS 2 , and monitoring the microstructure evolution during the growth of NaCl and CuSO 4 crystals. Not only addresses existing limitations in material characterization but also offers avenues for exploration.

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Recent Progress in Sparsity-Regularization Based Imaging Method for Near-Field 3D SAR

Zhan,

Zhang,

et al. 2023

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

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Learning-Based Split Unfolding Framework for 3-D mmW Radar Sparse Imaging

Cited by 8 publications

References 50 publications

Conical Differential Range Based Back-Projection Algorithm for Concealed Object Detection with Three-Dimensional mmW Imaging

Conical Differential Range Based Back-Projection Algorithm for Concealed Object Detection with Three-Dimensional mmW Imaging

Deep Learning Infrared Holography with Transformer for Crystal Material Characterization

Recent Progress in Sparsity-Regularization Based Imaging Method for Near-Field 3D SAR

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