Over the past three decades a new spectroscopic technique with unique possibilities has emerged. Based on coherent and time-resolved detection of the electric field of ultrashort radiation bursts in the far-infrared, this technique has become known as terahertz time-domain spectroscopy (THz-TDS). In this review article the authors describe the technique in its various implementations for static and time-resolved spectroscopy, and illustrate the performance of the technique with recent examples from solid-state physics and physical chemistry as well as aqueous chemistry. Examples from other fields of research, where THz spectroscopic techniques have proven to be useful research tools, and the potential for industrial applications of THz spectroscopic and imaging techniques are discussed.

Terahertz spectroscopy and imaging – Modern techniques and applications

The advent of negative index materials has spawned extensive research into metamaterials over the past decade. Metamaterials are attractive not only for their exotic electromagnetic properties, but also their promise for applications. A particular branch–the metamaterial perfect absorber (MPA)–has garnered interest due to the fact that it can achieve unity absorptivity of electromagnetic waves. Since its first experimental demonstration in 2008, the MPA has progressed significantly with designs shown across the electromagnetic spectrum, from microwave to optical. In this Progress Report we give an overview of the field and discuss a selection of examples and related applications. The ability of the MPA to exhibit extreme performance flexibility will be discussed and the theory underlying their operation and limitations will be established. Insight is given into what we can expect from this rapidly expanding field and future challenges will be addressed.

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/adma.201200674

Metamaterial Electromagnetic Wave Absorbers

Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas. This, in turn, necessitates a fresh look on many of the basics of CS. The random matrix measurement operator must be replaced by more structured sensing architectures that correspond to the characteristics of feasible acquisition hardware. The standard sparsity prior has to be extended to include a much richer class of signals and to encode broader data models, including continuous-time signals. In our overview, the theme is exploiting signal and measurement structure in compressive sensing. The prime focus is bridging theory and practice; that is, to pinpoint the potential of structured CS strategies to emerge from the math to the hardware. Our summary highlights new directions as well as relations to more traditional CS, with the hope of serving both as a review to practitioners wanting to join this emerging field, and as a reference for researchers that attempts to put some of the existing ideas in perspective of practical applications.

Structured Compressed Sensing: From Theory to Applications

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.

Compressive ghost imaging

We describe methods for learning dictionaries that are appropriate for the representation of given classes of signals and multisensor data. We further show that dimensionality reduction based on dictionary representation can be extended to address specific tasks such as data analy sis or classification when the learning includes a class separability criteria in the objective function. The benefits of dictionary learning clearly show that a proper understanding of causes underlying the sensed world is key to task-specific representation of relevant information in high-dimensional data sets.

Dictionary Learning

We describe a terahertz imaging system that uses a single pixel detector in combination with a series of random masks to enable high-speed image acquisition. The image formation is based on the theory of compressed sensing, which permits the reconstruction of a N-by-N pixel image using much fewer than N2 measurements. This approach eliminates the need for raster scanning of the object or the terahertz beam, while maintaining the high sensitivity of a single-element detector. We demonstrate the concept using a pulsed terahertz time-domain system and show the reconstruction of both amplitude and phase-contrast images. The idea of compressed sensing is quite general and could also be implemented with a continuous-wave terahertz source.

/pdf/a-single-pixel-terahertz-imaging-system-based-on-compressed-2zw4tugxrl.pdf

A single-pixel terahertz imaging system based on compressed sensing

There is great interest in sources of coherent radiation in the mid-wave infrared (3–5 μm), and instruments based on fiber can offer major practical advantages. This range, and much broader, can be covered easily by supercontinuum generation in soft glass fibers, but with low power spectral density. For applications that require intense ultrashort pulses, fiber sources are quite limited. In this Letter, we report a fiber-based system that generates 100 fs pulses with 5 nJ energy, continuously wavelength-tunable over 2–4.3 μm through the soliton self-frequency shift (SSFS) in fluoride fibers. The pulse energies are 2 orders of magnitude higher than those previously achieved by SSFS, around 3 μm, and the range of wavelengths is extended by 1000 nm. Peak power ranges from 20 to 75 kW are achieved across the tuning range. Numerical simulations are in good agreement with the experimental results, and indicate the potential for few-cycle soliton generation out to 5.6 μm. Fiber-integrated sources of femtosecond pulses tunable across this region should be valuable for mid-infrared applications.

Generation of intense 100 fs solitons tunable from 2 to 43 μm in fluoride fiber

Optical imaging plays a major role in both basic biological research and clinical diagnostics, providing noninvasive or minimally invasive microscopic imaging capability to investigate biological tissues. Optical image acquisition through significant depths of biological tissues, however, presents a major challenge since tissue is extremely heterogeneous and the strong scattering of the various tissue components has restricted high-resolution optical imaging to superficial layers. Multiphoton microscopy (MPM) has significantly extended the penetration depth of high-resolution optical imaging, particularly for in vivo applications. Multiphoton imaging critically depends on ultrafast technologies, particularly pulsed excitation sources. In this paper, the basics of deep tissue MPM and its improvements utilizing soliton self-frequency shift (SSFS) are reviewed. Wavelength tunable, high-energy soliton generation through SSFS in large-mode-area (LMA) fibers and photonic crystal rods is presented. The application of these solitons to MPM enables noninvasive imaging in biological tissues with unprecedented depth. The main characteristics of the excitation source for deep tissue MPM, such as wavelength, pulse energy, and repetition rate, are discussed.

Advanced Fiber Soliton Sources for Nonlinear Deep Tissue Imaging in Biophotonics

Optical imaging is important for understanding brain function. However, established methods with high spatiotemporal resolution are limited by the potential for laser damage to living tissues. We describe an adaptive femtosecond excitation source that only illuminates the region of interest, which leads to a 30-fold reduction in the power requirement for two- or three-photon imaging of brain activity in awake mice for improved high-speed longitudinal neuroimaging.

/pdf/an-adaptive-excitation-source-for-high-speed-multiphoton-gp20qqsi4m.pdf

An adaptive excitation source for high-speed multiphoton microscopy

Deep tissue multiphoton imaging requires high peak power to enhance signal and low average power to prevent thermal damage. Both goals can be advantageously achieved through laser repetition rate tuning instead of simply adjusting the average power. We show that the ideal repetition rate for deep two-photon imaging in the mouse brain is between 1 and 10 MHz, and we present a fiber-based source with an arbitrarily tunable repetition rate within this range. The performance of the new source is compared to a mode-locked Ti:Sapphire (Ti:S) laser for in vivo imaging of mouse brain vasculature. At 2.5 MHz, the fiber source requires 5.1 times less average power to obtain the same signal as a standard Ti:S laser operating at 80 MHz.

Kriti Charan

Papers

A single-pixel terahertz imaging system based on compressed sensing

Generation of intense 100 fs solitons tunable from 2 to 43 μm in fluoride fiber

Advanced Fiber Soliton Sources for Nonlinear Deep Tissue Imaging in Biophotonics

An adaptive excitation source for high-speed multiphoton microscopy

Fiber-based tunable repetition rate source for deep tissue two-photon fluorescence microscopy.