A single-pixel terahertz imaging system based on compressed sensing

Chan, Wai Lam; Charan, Kriti; Takhar, Dharmpal; Kelly, Kevin F.; Baraniuk, Richard G.; Mittleman, Daniel M.

doi:10.1063/1.2989126

Cited by 672 publications

(374 citation statements)

References 16 publications

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“…Another option therefore is to alter the imaging paradigm itself, relying on the sparsity of spatial frequencies in most images to perform compressed sensing (CS). First demonstrated at THz frequencies in 2008 (47,48) this technique has since been demonstrated, among others, by using either random mechanically developed and switched masks (49), a spinning disk set (50), electrically gated graphene modulator arrays (51) and by photoexciting a silicon substrate with an optical pattern (52). The techniques demonstrated here rely on a binary state for each pixel, i.e., the pixels are either on and off, in order to eventually separate the response of each pixel through the linear combinations recorded in the measurements.…”

Section: Imaging Speedmentioning

confidence: 99%

Recent advances in terahertz technology for biomedical applications

Sun¹,

He²,

Li³

et al. 2017

Quant. Imaging Med. Surg.

219

113

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Terahertz instrumentation has improved significantly in recent years such that THz imaging systems have become more affordable and easier to use. THz systems can now be operated by non-THz experts greatly facilitating research into many potential applications. Due to the non-ionising nature of THz light and its high sensitivity to soft tissues, there is an increasing interest in biomedical applications including both in vivo and ex vivo studies. Additionally, research continues into understanding the origin of contrast and how to interpret terahertz biomedical images. This short review highlights some of the recent work in these areas and suggests some future research directions.

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Section: Imaging Speedmentioning

confidence: 99%

Recent advances in terahertz technology for biomedical applications

Sun¹,

He²,

Li³

et al. 2017

Quant. Imaging Med. Surg.

219

113

View full text Add to dashboard Cite

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“…The algorithm is based on compressed sensing (or compressive sampling, CS) [9,10], an advanced sampling and reconstruction technique which has been recently implemented in several elds of imaging. Examples for such are magnetic resonance imaging [11], astronomy [12], THz imaging [13], and single-pixel cameras [14]. The main idea behind CS is to exploit the redundancy in the structure of most natural signals/objects to reduce the number of measurements required for faithful reconstruction.…”

mentioning

confidence: 99%

Compressive ghost imaging

Katz¹,

Bromberg²,

Silberberg³

2009

Applied Physics Letters

881

495

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We describe an advanced image reconstruction algorithm for pseudothermal ghost imaging, reducing the number of measurements required for image recovery by an order of magnitude. The algorithm is based on compressed sensing, a technique that enables the reconstruction of an N -pixel image from much less than N measurements. We demonstrate the algorithm using experimental data from a pseudothermal ghost-imaging setup. The algorithm can be applied to data taken from past pseudothermal ghost-imaging experiments, improving the reconstruction's quality.Ghost imaging (GI) has emerged a decade ago as an imaging technique which exploits the quantum nature of light, and has been in the focus of many studies since [1, and references therin]. In GI an object is imaged even though the light which illuminates it is collected by a single-pixel detector which has no spatial resolution (a bucket detector). This is done by correlating the intensities measured by the bucket detector with an image of the eld which impinges upon the object. GI was originally performed using entangled photon pairs [2], and later on was realized with classical light sources [3,4,5,6]. The demonstrations of GI with classical light sources, and especially pseudothermal sources, triggered an ongoing e ort to implement GI for various sensing applications [4,7]. However, one of the main drawbacks of pseudothermal GI is the long acquisition times required for reconstructing images with a good signal-to-noise ratio (SNR) [1,8].In this work we propose an advanced reconstruction algorithm for pseudothermal GI, which reduces signicantly the required acquisition times. The algorithm is based on compressed sensing (or compressive sampling, CS) [9,10], an advanced sampling and reconstruction technique which has been recently implemented in several elds of imaging. Examples for such are magnetic resonance imaging [11], astronomy [12], THz imaging [13], and single-pixel cameras [14]. The main idea behind CS is to exploit the redundancy in the structure of most natural signals/objects to reduce the number of measurements required for faithful reconstruction. Here we show that applying a CS-based reconstruction algorithm to data taken from conventional pseudothermal GI measurements dramatically improves the SNR of the reconstructed images and thus allows for shorter acquisition times.In conventional pseudothermal GI, an object is illuminated by a speckle eld generated by passing a laser beam through a rotating di user [ Fig. 1(a)]. For each phase realization r of the di user, the speckle eld I r (x, y) which impinges on the object is imaged. This is done by splitting the beam before the object to an 'object arm' and a 'reference arm', and placing a CCD camera at the refer- * Electronic address: ori.katz@weizmann.ac.il Figure 1: (Color online) (a) Standard pseudothermal GI twodetectors setup. A copy of the speckle eld which impinges on the object is imaged with a CCD camera, and correlated with the intensity measured by a bucket detector. (b) The computational GI singl...

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“…The sparsity property of a magnetic resonance image (MRI) made the CS technique a successful application in improving the image quality through the reduction of the measurement amount [1]. As for application in photography, CS allows enhancement in the acquisition speed of the singel-pixel Terahertz camera [2]. In high-speed applications like the 1080p HD video, CS helps relieve the burden on data acquisition process, as the number of acquired datum is significantly fewer [3].…”

Section: Introductionmentioning

confidence: 99%

“…signals that have only a few non-zero values) [1,2,3], as it allows for reconstruction of such a signal from much fewer measurements than required by the conventional sampling at the Nyquist rate. Therefore, CS makes it possible to dramatically reduce resource consumption, processing time, and power consumption required for data acquisition, transmission, and manipulation in diverse applications.…”

Section: Introductionmentioning

confidence: 99%

High-performance ASIC realization of orthogonal matching pursuit algorithm

Nguyen

Park

2018

IEICE Electron. Express

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Compressive sensing (CS) is perceived as a breakthrough in sampling theory, as it proves that sparse signals can be reconstructed from fewer samples than required by the Shannon-Nyquist theorem. However, CS can hardly be applied to real-time applications because reconstruction algorithms are computationally demanding. To tackle this problem, in this paper, we propose a new high-speed architecture for implementing the orthogonal matching pursuit (OMP), which is one of the most popular algorithms for CS reconstruction. Specifically, a novel pipelined systolic architecture and an optimized scheduling strategy are proposed. From the synthesis results, we find that the proposed design takes 1.638 µs to reconstruct 16-sparse signal, which is 19.2 times faster than the existing VLSI implementation of the OMP.

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A single-pixel terahertz imaging system based on compressed sensing

Cited by 672 publications

References 16 publications

Recent advances in terahertz technology for biomedical applications

Recent advances in terahertz technology for biomedical applications

Compressive ghost imaging

High-performance ASIC realization of orthogonal matching pursuit algorithm

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