Low-cost single-pixel 3D imaging by using an LED array

Salvador-Balaguer, Eva; Carmona, Pedro; Chabert, Carlos; Pla, Filiberto; Láncis, Jesús; Tajahuerce, Enrique

doi:10.1364/oe.26.015623

Cited by 60 publications

(21 citation statements)

References 45 publications

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“…However, the higher refreshing rate of the DMD results in a lower signal-to-noise ratio in single-pixel measurements, as the integral time for each single-pixel measurement will be shorter. It is reasonable to use a high-speed and high-flux spatial light modulator (for example, high-speed LED matrix [27,28]) so as to ensure a high signal-to-noise ratio even when the refreshing rate of the spatial light modulator is high.…”

Section: Discussionmentioning

confidence: 99%

Image-free classification of fast-moving objects using “learned” structured illumination and single-pixel detection

Zhang¹,

Li²,

Zheng³

et al. 2020

Opt. Express

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Objects classification generally relies on image acquisition and analysis. Real-time classification of high-speed moving objects is challenging, as both high temporal resolution in image acquisition and low computational complexity in objects classification algorithms are required. Here we propose and experimentally demonstrate an approach for real-time moving objects classification without image acquisition. As objects classification algorithms rely on the feature information of objects, we propose to use spatial light modulation to acquire the feature information directly rather than performing image acquisition followed by features extraction. A convolutional neural network is designed and trained to learn the spatial features of the target objects. The trained network can generate structured patterns for spatial light modulation. Using the resulting structured patterns for spatial light modulation, the feature information of target objects can be compressively encoded into a short light intensity sequence. The resulting one-dimensional signal is collected by a single-pixel detector and fed to the convolutional neural network for objects classification. As experimentally demonstrated, the proposed approach can achieve accurate and real-time classification of fast moving objects. The proposed method has potential applications in the fields where fast moving objects classification in real time and for long duration is required.

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Section: Discussionmentioning

confidence: 99%

Image-free classification of fast-moving objects using “learned” structured illumination and single-pixel detection

Zhang¹,

Li²,

Zheng³

et al. 2020

Opt. Express

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“…There are other types of SLMs that can be used to establish a single-pixel imaging system, and one can select the proper modulators in different regions of interest. A summarized table (Table 1) is thus given to compare these modulators, such as DMD [86,87], LCD or LCOS [88,89], mechanical mask [90,91], customized diffuser [92,93], light-emitting diode (LED) arrays [94,95] and optical phase arrays (OPA) [96,97]. From the given table, it can be seen that the LCOS and DMD potentially satisfy the continuously increasing trend of miniature imaging systems due to their compactness.…”

Section: The System Architecturementioning

confidence: 99%

Single-Pixel MEMS Imaging Systems

Zhou

Lim

2020

Micromachines

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Single-pixel imaging technology is an attractive technology considering the increasing demand of imagers that can operate in wavelengths where traditional cameras have limited efficiency. Meanwhile, the miniaturization of imaging systems is also desired to build affordable and portable devices for field applications. Therefore, single-pixel imaging systems based on microelectromechanical systems (MEMS) is an effective solution to develop truly miniaturized imagers, owing to their ability to integrate multiple functionalities within a small device. MEMS-based single-pixel imaging systems have mainly been explored in two research directions, namely the encoding-based approach and the scanning-based approach. The scanning method utilizes a variety of MEMS scanners to scan the target scenery and has potential applications in the biological imaging field. The encoding-based system typically employs MEMS modulators and a single-pixel detector to encode the light intensities of the scenery, and the images are constructed by harvesting the power of computational technology. This has the capability to capture non-visible images and 3D images. Thus, this review discusses the two approaches in detail, and their applications are also reviewed to evaluate the efficiency and advantages in various fields.

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“…However, the optical setup has certain experimental complexity such as precise alignment requirement of optical elements, fabrication difficulty of HOE and availability of laser source. Different from the coherent imaging system [11,12], an incoherent optical system such as a single-pixel imaging (SPI) system [16][17][18][19][20][21] will have significantly lower experimental complexity. In fact, this has been revealed in some previous works about optical computing [22] and holography [23].…”

Section: Introductionmentioning

confidence: 99%

Visual cryptography in single-pixel imaging

Jiao¹,

Feng²,

Gao³

et al. 2020

Opt. Express

121

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Two novel visual cryptography (VC) schemes are proposed by combining VC with single-pixel imaging (SPI) for the first time. It is pointed out that the overlapping of visual key images in VC is similar to the superposition of pixel intensities by a single-pixel detector in SPI. In the first scheme, QR-code VC is designed by using opaque sheets instead of transparent sheets. The secret image can be recovered when identical illumination patterns are projected onto multiple visual key images and a single detector is used to record the total light intensities. In the second scheme, the secret image is shared by multiple illumination pattern sequences and it can be recovered when the visual key patterns are projected onto identical items. The application of VC can be extended to more diversified scenarios by our proposed schemes.

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Low-cost single-pixel 3D imaging by using an LED array

Cited by 60 publications

References 45 publications

Image-free classification of fast-moving objects using “learned” structured illumination and single-pixel detection

Image-free classification of fast-moving objects using “learned” structured illumination and single-pixel detection

Single-Pixel MEMS Imaging Systems

Visual cryptography in single-pixel imaging

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