Deep Learning Optimized Terahertz Single-Pixel Imaging

Zhu, Yongle; She, Rongbin; Liu, Wenquan; Lu, Yuanfu; Li, Guangyuan

doi:10.1109/tthz.2021.3132160

Cited by 16 publications

(8 citation statements)

References 32 publications

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“…Graphene modulator arrays are used for THz imaging with video-rate modulation speed and 16 × 16 pixels [ 84 ]. In addition to the compressed sensing technique, the sampling time can be further reduced by deep-learning neural networks while retaining high imaging quality and good signal-to-noise ratio [ 115 , 116 ].…”

Section: Applications and Challengesmentioning

confidence: 99%

Recent Progress of Terahertz Spatial Light Modulators: Materials, Principles and Applications

2022

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Terahertz (THz) technology offers unparalleled opportunities in a wide variety of applications, ranging from imaging and spectroscopy to communications and quality control, where lack of efficient modulation devices poses a major bottleneck. Spatial modulation allows for dynamically encoding various spatial information into the THz wavefront by electrical or optical control. It plays a key role in single-pixel imaging, beam scanning and wavefront shaping. Although mature techniques from the microwave and optical band are not readily applicable when scaled to the THz band, the rise of metasurfaces and the advance of new materials do inspire new possibilities. In this review, we summarize the recent progress of THz spatial light modulators from the perspective of functional materials and analyze their modulation principles, specifications, applications and possible challenges. We envision new advances of this technique in the near future to promote THz applications in different fields.

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Section: Applications and Challengesmentioning

confidence: 99%

Recent Progress of Terahertz Spatial Light Modulators: Materials, Principles and Applications

2022

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“…Due to its function of adopting a single-pixel detector to collect the intensity of an object illuminated by a sequence of masked patterns, single-pixel imaging (SPI) is attractive in diverse applications such as ultrafast imaging [1,2], hyperspectral imaging [3,4], remote tracking [5,6], and three-dimensional imaging [7,8] for its low cost, high signal-to-noise ratio, and broadband operation [9][10][11]. The SPI techniques are especially important for wavelengths with expensive multi-pixel detectors, including terahertz [12][13][14][15], infrared [16][17][18][19], and X-ray [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Single-Pixel Imaging Based on Deep Learning Enhanced Singular Value Decomposition

Deng,

She,

Liu

et al. 2024

Sensors

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We propose and demonstrate a single-pixel imaging method based on deep learning network enhanced singular value decomposition. The theoretical framework and the experimental implementation are elaborated and compared with the conventional methods based on Hadamard patterns or deep convolutional autoencoder network. Simulation and experimental results show that the proposed approach is capable of reconstructing images with better quality especially under a low sampling ratio down to 3.12%, or with fewer measurements or shorter acquisition time if the image quality is given. We further demonstrate that it has better anti-noise performance by introducing noises in the SPI systems, and we show that it has better generalizability by applying the systems to targets outside the training dataset. We expect that the developed method will find potential applications based on single-pixel imaging beyond the visible regime.

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“…The generative adversarial network (GAN) has also been used for SPI, in which the discriminator introduces adversarial error into the loss function. The GAN-based method is able to achieve better results due to the advanced adversarial training strategy. − Other studies are mainly carried out from the aspects of learning method, , network structure, , and the combination with specific applications. − …”

Section: Introductionmentioning

confidence: 99%

VGenNet: Variable Generative Prior Enhanced Single Pixel Imaging

et al. 2023

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Single-pixel imaging (SPI) is an emerging imaging methodology that converts a two-or even three-dimensional image acquisition problem into a one-dimensional (1D) temporal-signal detection problem. Thus, it is crucially important to develop efficient SPI techniques for image reconstruction from the 1D measurements, in particular, an undersampled one. Recently, various studies have demonstrated the superiority of deep learning for SPI. However, due to the generalization issue, conventional datadriven deep learning is a task-specific approach. One needs to retrain the neural network for different SPI imaging problems and different types of objects. Here, we propose a variable generative network enhanced SPI algorithm (VGenNet) by incorporating a model-driven fine-tuning process into a generative model that may have been trained for other tasks. VGenNet simultaneously updates the input vector and the weights in a generator to generate feasible solutions that reproduce the raw measurements. We demonstrate the proposed technique with indoor SPI and outdoor 3D single-pixel LiDAR experiments. Our results show that high-quality images can be reconstructed at low sampling ratios under different system configurations, demonstrating the good performance and flexibility of VGenNet. Overall, the proposed VGenNet is a general framework to take advantage of both the data and physics priors, allowing the direct use of a pretrained generative model to solve various inverse imaging problems.

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Deep Learning Optimized Terahertz Single-Pixel Imaging

Cited by 16 publications

References 32 publications

Recent Progress of Terahertz Spatial Light Modulators: Materials, Principles and Applications

Recent Progress of Terahertz Spatial Light Modulators: Materials, Principles and Applications

Single-Pixel Imaging Based on Deep Learning Enhanced Singular Value Decomposition

VGenNet: Variable Generative Prior Enhanced Single Pixel Imaging

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