Abstract-Time-domain terahertz compressive imaging

Luca Zanotto

https://arxiv.org/abs/1911.09545

We present an implementation of the single-pixel imaging approach into a terahertz (THz) time-domain spectroscopy (TDS) system. We demonstrate the indirect coherent reconstruction of THz temporal waveforms at each spatial position of an object, without the need of mechanical raster-scanning. First, we exploit such temporal information to realize (far-field) time-of-flight images. In addition, as a proof of concept, we apply a typical compressive sensing algorithm to demonstrate image reconstruction with less than 50% of the total required measurements. Finally, the access to frequency domain is also demonstrated by reconstructing spectral images of an object featuring an absorption line in the THz range. The combination of single-pixel imaging with compressive sensing algorithms allows to reduce both complexity and acquisition time of current THz-TDS imaging systems.

Abstract-Reconstruction Methods in THz Single-pixel Imaging

Martin Burger, Janic Föcke, Lukas Nickel, Peter Jung, Sven Augustin

https://arxiv.org/abs/1903.08893

The aim of this paper is to discuss some advanced aspects of image reconstruction in single-pixel cameras, focusing in particular on detectors in the THz regime. We discuss the reconstruction problem from a computational imaging perspective and provide a comparison of the effects of several state-of-the art regularization techniques.
Moreover, we focus on some advanced aspects arising in practice with THz cameras, which lead to nonlinear reconstruction problems: the calibration of the beam reminiscent of the Retinex problem in imaging and phase recovery problems. Finally we provide an outlook to future challenges in the area.

A revolutionary imaging technique uses a single pixel to fill our terahertz blind spot

ORIGINAL IMAGE: RECONSTRUCTION METHODS IN THZ SINGLE-PIXEL IMAGING; EDITED BY MIT TECHNOLOGY REVIEW

https://www.technologyreview.com/s/613278/a-revolutionary-imaging-technique-uses-a-single-pixel-to-fill-our-terahertz-blind-spot/

Terahertz waves provide a unique view of the world but have always been hard to detect. That looks set to change.

At almost every wavelength engineers have electromagnetic
antennae that can detect and record the waves and create exotic images of the world at radio, microwave, infrared, visible, and x-ray frequencies.

But there is a blind spot in this spectrum. The technology is still in its infancy to detect radiation with a wavelength of between 1 and 0.3 millimeters and a frequency of about a terahertz. The equipment that can detect such radiation is bulky and expensive and the resulting images poor. Hence the “blind spot,” which engineers have called the terahertz gap.

A better way to capture these wavelengths is desperately needed, not least to gain a new window into the universe.

Today Martin Burger at the University of Munster in Germany and a few colleagues describe a revolutionary new imaging technique—compressed sensing—that is set to make this part of the electromagnetic spectrum more accessible. Applying the technique to terahertz waves is likely to change the way we see our world and the universe beyond.

First, some background. Terahertz waves pass through clothes but not through skin or metal. If your eyes could pick them up, people would appear naked but decorated with keys and coins but perhaps also knives and guns. So this kind of imaging has significant security applications, not to mention privacy implications.

Terahertz frequencies are difficult to detect because they sit on the electromagnetic spectrum between microwaves and infrared light, and there is an important difference between the way these types of radiation can be detected.

Microwaves, like radio waves, are made by accelerating a charge back and forth at the required frequency—in this case, up to about 300 gigahertz.  Detecting microwaves exploits the same process in reverse.

By contrast, infrared waves, like light, are made by making an electron in a suitable material jump between two electronic levels. This generates infrared light when the energy required to make the jump is equivalent to the energy of an infrared photon. The same process in reverse can also detect infrared photons.

Making and detecting terahertz waves is hard because they sit in the middle where neither technique works particularly well. It's tough to accelerate charges at terahertz frequencies. And materials with the required bandgap to create terahertz photons are difficult to find, and those that qualify often have to be cooled to cryogenic temperatures. That’s why terahertz detectors tend to be bulky, expensive, and hard to manage.

But compressed sensing can help, say Burger and co. In recent years, this technique has taken the world of imaging by storm because it allows a single pixel to record high-resolution images, even in 3-D.

The technique works by randomizing the reflected light from a scene and then recording it using a single pixel. The randomization can be done in various ways, but a common approach is to pass the light through a digital array called a spatial light modulator that displays a random pattern of transparent and opaque pixels. The randomization process is then repeated and the light field recorded again, and the entire process is repeated many times to generate many data points.

At first it’s hard to see how this can produce an image—after all, the light field is randomized. But the data points aren’t completely random. Indeed, each data point is correlated with all others because they all come from the same source—the original scene. So by finding this correlation, it is possible to recreate the original image.

It turns out that computer scientists have a variety of algorithms that can do this kind of number crunching. And the result is an image with a resolution that depends on the number of data points recorded by the pixel. The more data, the higher the resolution.

That has immediate application for terahertz imaging. Until now, the only way to create a 2-D image was to use an array of terahertz detectors or to scan a single detector back and forth to map out the light field. Neither technique is satisfactory because of the unwieldy size of terahertz detectors.

But compressed sensing offers an alternative: using a single terahertz detector to record multiple data points through a spatial light modulator that randomizes the terahertz light. That works well for visible and infrared light, and numerous groups have begun to exploit it successfully.

However, terahertz light introduces some additional complexities. For example, because terahertz waves are two or three orders of magnitude bigger than optical waves, they more easily diffract. This effect and others introduce distortions that make the image reconstruction much harder. It is this challenge of image reconstruction that Burger and co have taken on.

Their results are impressive. The team shows how various techniques can significantly improve the quality of resulting images. “The compressed-sensing approach based on single-pixel imaging has great potential to decrease measurement time and effort in THz imaging,” they say.

However, there are challenges ahead. One problem is in dealing with images made from more than one frequency of terahertz light. This kind of analysis is particularly important because it provides spectroscopic information about the chemical makeup of the subject in the image—for example, whether a crystalline powder is flour or some kind of drug. 

But this requires different types of mask. So a challenge is to find the best way to create a hyperspectral image using the smallest number of masks.

Nonetheless, Burger and co are optimistic that compressed sensing will allow rapid progress in finally closing the terahertz gap.

Ref: arxiv.org/abs/1903.08893 : Reconstruction Methods in THz Single-Pixel Imaging

Abstract-Terahertz wave near-field compressive imaging with a spatial resolution of over λ/100

Si-Chao Chen, Liang-Hui Du, Kun Meng, Jiang Li, Zhao-Hui Zhai, Qi-Wu Shi, Ze-Ren Li, and Li-Guo Zhu

https://www.osapublishing.org/ol/abstract.cfm?uri=ol-44-1-21

We demonstrate terahertz (THz) wave near-field imaging with a spatial resolution of ∼4.5  μm$\sim 4.5\mathrm{\mu m}$using single-pixel compressive sensing enabled by femtosecond-laser (𝑓𝑠$fs$-laser) driven vanadium dioxide (VO2${\mathrm{VO}}_2$)-based spatial light modulator. By 𝑓𝑠$fs$-laser patterning a 180 nm thick VO2${\mathrm{VO}}_2$ nanofilm with a digital micromirror device, we spatially encode the near-field THz evanescent waves. With single-pixel Hadamard detection of the evanescent waves, we reconstructed the THz wave near-field image of an object from a serial of encoded sequential measurements, yielding improved signal-to-noise ratio by one order of magnitude over a raster-scanning technique. Further, we demonstrate that the acquisition time was compressed by a factor of over four with 90% fidelity using a total variation minimization algorithm. The proposed THz wave near-field imaging technique inspires new and challenging applications such as cellular imaging.

© 2018 Optical Society of America

Abstract-Compressive Sensing Imaging at Sub-THz Frequency in Transmission Mode

Vedat Ali Özkan, Yıldız Menteşe, Taylan Takan, Asaf Behzat Şahin, Hakan Altan

https://link.springer.com/chapter/10.1007/978-94-024-1093-8_7

Due to lack of widespread array imaging techniques in the THz range, point detector applications coupled with spatial modulation schemes are being investigated using compressive sensing (CS) techniques. CS algorithms coupled with innovative spatial modulation schemes which allow the control of pixels on the image plane from which the light is focused onto single pixel THz detector has been shown to rapidly generate images of objects. Using a CS algorithm, the image of an object can be reconstructed rapidly. Using a multiplied Schottky diode based multiplied millimeter wave source working at 113 GHz, a metal cutout letter F, which served as the target was illuminated in transmission. The image is spatially discretized by laser machined, 10 × 10 pixel metal apertures to demonstrate the technique of spatial modulation coupled with compressive sensing. The image was reconstructed by modulating the source and measuring the transmitted flux through the metal apertures using a Golay cell. Experimental results were compared to reference image to assess reconstruction performance using χ2 index. It is shown that a satisfactory image is reconstructed below the Nyquist rate which demonstrates that after taking into account the light intensity distribution at the image plane, compressive sensing is an advantageous method to be employed for remote sensing with point detectors.

Abstract-Compressive sensing for direct millimeter-wave holographic imaging

Compressive sensing for direct millimeter-wave holographic imaging Lingbo Qiao, Yingxin Wang, Zongjun Shen, Ziran Zhao, and Zhiqiang Chen  »View Author Affiliations

http://www.opticsinfobase.org/ao/abstract.cfm?uri=ao-54-11-3280

Applied Optics, Vol. 54, Issue 11, pp. 3280-3289 (2015)
http://dx.doi.org/10.1364/AO.54.003280

View Full Text Article

Enhanced HTML    Acrobat PDF (1064 KB)

Direct millimeter-wave (MMW) holographic imaging, which provides both the amplitude and phase information by using the heterodyne mixing technique, is considered a powerful tool for personnel security surveillance. However, MWW imaging systems usually suffer from the problem of high cost or relatively long data acquisition periods for array or single-pixel systems. In this paper, compressive sensing (CS), which aims at sparse sampling, is extended to direct MMW holographic imaging for reducing the number of antenna units or the data acquisition time. First, following the scalar diffraction theory, an exact derivation of the direct MMW holographic reconstruction is presented. Then, CS reconstruction strategies for complex-valued MMW images are introduced based on the derived reconstruction formula. To pursue the applicability for near-field MMW imaging and more complicated imaging targets, three sparsity bases, including total variance, wavelet, and curvelet, are evaluated for the CS reconstruction of MMW images. We also discuss different sampling patterns for single-pixel, linear array and two-dimensional array MMW imaging systems. Both simulations and experiments demonstrate the feasibility of recovering MMW images from measurements at 1/2 or even 1/4 of the Nyquist rate.

© 2015 Optical Society of America

Abstract-Compressed sensing of terahertz radar azimuth-elevation imaging

Hongqiang Wang, Ruijun Wang, Bin Deng, Wuge Su

National University of Defense Technology, College of Electronic Science and Engineering, No. 109, Deya Road, Changsha 410073, China

http://electronicimaging.spiedigitallibrary.org/article.aspx?articleid=2174076%20&journalid=94

J. Electron. Imaging. 24(1), 013035 (Feb 24, 2015). doi:10.1117/1.JEI.24.1.013035

There is increasing interest in high-resolution radar imaging of targets, and the recent development of terahertz (THz) imaging technique provides the depiction ability of targets in detail. The compressed sensing theory is introduced into terahertz radar azimuth-elevation imaging for facilitating the increasing sampling pressure and obtaining the improved imagery by exploiting the block sparse structure of a target’s reflectivity distribution. Compared with the conventional processing of even block partition in block sparsity, a block-coherence definition for uneven block partition is proposed as a sensing configuration quality parameter, and its relationship to the imagery reconstruction performance is verified. Further, a contrast metric for evaluating the improved image of uneven block partition is discussed without the knowledge of a true imaging result. The electromagnetic calculation data are used for the verification of imaging.

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10 Patents on Compressive Sensing Issued to InView Technology Corporation in 2014

http://inviewcorp.com/news/10-patents-on-compressive-sensing-issued-to-inview-technology-corporation-in-2014/#.VOvZj4ZHarU

And there are more on the way!

Ten patents were issued to InView Technology Corporation in 2014 covering the implementation and improvement of its Compressive Sensing camera architecture and algorithms.  With its initial product, the InView210™, InView has developed the world’s first SWIR camera based on the computational imaging architecture of compressive sensing.  InView continues to enhance its IP portfolio of 12 issued patents and 10 additional patent applications surrounding the implementation of its unique imaging modality, whose mathematical foundations were developed only within the last decade.  The foundational patent on the single-pixel camera architecture was issued in 2012 to Rice University and, along with other related patents, is exclusively licensed by InView.

Current development projects funded by DoD and NSF grants include a compressive video camera with high speed event detection capabilities.  InView is also developing a multi-spectral camera that can create false-color images combining visible and near infrared wavebands.

InView continues to innovate its compressive sensing architecture and data acquisition strategies to take advantage of its unique computational platform and welcomes licensing and investment inquiries.

Patents Issued in 2014

US 8,634,009 Dynamic range optimization in a compressive imaging system        January 21, 2014

Using differential detection methods and adjustable gain control to maximize the number of bits associated with the digitization of the compressive sensing measurement signal.

US 8,717,463 Adaptively filtering compressive imaging measurements to attenuate noise     May 6, 2014

Analog and digital filtering techniques applied to compressive sensing measurements to reduce zero-mean noise.

US 8,717,466 Dual-port measurements of light reflected from micromirror array              May 6, 2014

A unique way of making complementary measurements from the modulator used in the compressive camera architecture for inferring variations in light levels that contribute to noise

US 8,717,484 TIR prism to separate incident light and modulated light in compressive imaging device          May 6, 2014

Use of an optical prism device that allows the light path to and from the modulator to be made more compact contributing to a reduction in the size of the compressive camera and the use of standardized lenses.

US 8,717,492 Focusing mechanisms for compressive imaging device     May 6, 2014

The computational aspect of compressive sensing is used for the manual and automatic focusing of compressive sensing cameras.

US 8,717,551 Adaptive search for atypical regions in incident light field and spectral classification of light in the atypical regions            May 6, 2014

Algorithms and implementations are disclosed for detecting and classifying regions in the field of view of a compressive camera that are anomalous.

US 8,760,542 Compensation of compressive imaging measurements based on measurements from power meter   June 24, 2014

Using power meter measurements of calibration patterns and other techniques to significantly decrease noise levels in compressive sensing measurements and increasing image quality.

US 8,860,835 Decreasing image acquisition time for compressive imaging devices         October 14, 2014

Mechanisms are disclosed for speeding up the compressive sensing data acquisition process by dividing the field of view into multiple spatial regions, creating several data streams and using multiple detectors in parallel.

US 8,885,073 Dedicated power meter to measure background light level in compressive imaging system   November 11, 2014

Using the signal from a simple dedicated power meter to enhance compressive sensing imaging.

US 8,922,688 Hot spot correction in a compressive imaging system       December 30, 2014

Adaptive control of the modulator within the compressive camera architecture provides the means for automatically aggregating or removing regions in the field of view of a compressive camera that are excessively bright or otherwise of interest or not typical for the scene.

Patents Issued in 2013

US 8,570,406 Low pass filtering of compressive imaging measurements to infer light level variations           October 29, 2013

A method for compensating for background light level variations experienced during compressive measurement acquisition by low-pass filtering the measurements.

US 8, 570,405 Determining light level variation in compressive imaging by injecting calibration patterns into pattern sequence     October 29, 2013

A method for compensating for background light level variations experienced during compressive measurement acquisition using calibration patterns within the series of modulation patterns for compressive imaging.

Issued in 2012

US 8,199,244  Method and apparatus for compressive imaging device

InView has exclusive license for this foundational patent on the single-pixel camera architecture from Rice University.