A dynamically reconfigurable terahertz array antenna for 2D-imaging applications

Nallappan, Kathirvel; Li, Jingwen; Guerboukha, Hichem; Markov, Andrey; Petrov, B. K.; Morris, Denis

doi:10.1109/pn.2017.8090603

Cited by 9 publications

(7 citation statements)

References 2 publications

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“…For the terahertz regime, this can be accomplished by three possible ways. First, having an array of photoconductive antennas [70][71][72], however this approach suffers from antenna cross-talk and inefficiency problems arising from the small working-area of the antennas whilst occupying a large area. Further, such antennas arrays have only been used as detectors.…”

Section: Spatially Patterned Thz Generationmentioning

confidence: 99%

Spatial Terahertz-Light Modulators for Single-Pixel Cameras

Stantchev¹,

Pickwell‐MacPherson²

2022

Terahertz Technology

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Terahertz imaging looks set to become an integral part of future applications from semiconductor quality control to medical diagnosis. This will only become a reality when the technology is sufficiently cheap and capabilities adequate to compete with others. Single-pixel cameras use a spatial light modulator and a detector with no spatial-resolution in their imaging process. The spatial-modulator is key as it imparts a series of encoding masks on the beam and the detector measures the dot product of each mask and the object, thereby allowing computers to recover an image via post-processing. They are inherently slower than parallel-pixel imaging arrays although they are more robust and cheaper, hence are highly applicable to the terahertz regime. This chapter dedicates itself to terahertz single-pixel cameras; their current implementations, future directions and how they compare to other terahertz imaging techniques. We start by outlining the competing imaging techniques, then we discuss the theory behind single-pixel imaging; the main section shows the methods of spatially modulating a terahertz beam; and finally there is a discussion about the future limits of such cameras and the concluding remarks express the authors’ vision for the future of single-pixel THz cameras.

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Section: Spatially Patterned Thz Generationmentioning

confidence: 99%

Spatial Terahertz-Light Modulators for Single-Pixel Cameras

Stantchev¹,

Pickwell‐MacPherson²

2022

Terahertz Technology

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“…Experimental results show that the algorithm has a fast convergence speed and small memory occupation, which is very attractive for large-scale optimization problems [26][27][28]. Based on a split Bregman iteration algorithmic framework, we first transform the optimization problem (11) (12) where  and  are regularization parameters. The optimization problem (12) can be solved by iteratively solving the problems ( 13) and ( 14): (14) In addition, optimization problem ( 13) is still difficult to solve directly and effectively because of its non-differentiability.…”

Section: For a Given Sample Imagementioning

confidence: 99%

“…To reduce the long time required for the image acquisition in terahertz imaging, many different techniques have been proposed for THz-TDS. Detector arrays have been used to speed up the image acquisition time limited by sequential data acquisition [10][11][12]. Although these devices can acquire real-time imaging, they suffer from high complexity and low sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Sparsity Model for Fast Terahertz Imaging

2021

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In order to shorten the long-term image acquisition time of the terahertz time domain spectroscopy imaging system while ensuring the imaging quality, a hybrid sparsity model (HSM) is proposed for fast terahertz imaging in this paper, which incorporates both intrinsic sparsity prior and nonlocal self-similarity constraints in a unified statistical model. In HSM, a weighted exponentiation shift-invariant wavelet transform is introduced to enhance the sparsity of the terahertz image. Simultaneously, the nonlocal self-similarity by means of the three-dimensional sparsity in the transform domain is exploited to ensure high-quality terahertz image reconstruction. Finally, a new split Bregman-based iteration algorithm is developed to solve the terahertz imaging model more efficiently. Experiments are presented to verify the effectiveness of the proposed approach.

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“…Numerous techniques have been proposed to solve the problem of long-time acquisition of THz-TDS technology. Terahertz detector arrays have shown great application potential in shortening long-time acquisition [10][11][12], but they suffer from high equipment and complexity costs. A Fourier based terahertz imaging method was proposed to achieve fast terahertz image reconstruction by using sparsity prior to the image [13].…”

Section: Introductionmentioning

confidence: 99%

Fast Terahertz Imaging Model Based on Group Sparsity and Nonlocal Self-Similarity

et al. 2022

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In order to solve the problems of long-term image acquisition time and massive data processing in a terahertz time domain spectroscopy imaging system, a novel fast terahertz imaging model, combined with group sparsity and nonlocal self-similarity (GSNS), is proposed in this paper. In GSNS, the structure similarity and sparsity of image patches in both two-dimensional and three-dimensional space are utilized to obtain high-quality terahertz images. It has the advantages of detail clarity and edge preservation. Furthermore, to overcome the high computational costs of matrix inversion in traditional split Bregman iteration, an acceleration scheme based on conjugate gradient method is proposed to solve the terahertz imaging model more efficiently. Experiments results demonstrate that the proposed approach can lead to better terahertz image reconstruction performance at low sampling rates.

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A dynamically reconfigurable terahertz array antenna for 2D-imaging applications

Cited by 9 publications

References 2 publications

Spatial Terahertz-Light Modulators for Single-Pixel Cameras

Spatial Terahertz-Light Modulators for Single-Pixel Cameras

Hybrid Sparsity Model for Fast Terahertz Imaging

Fast Terahertz Imaging Model Based on Group Sparsity and Nonlocal Self-Similarity

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