Snapshot Measurement Research Articles

Total variation regularized nonlocal low-rank tensor train for spectral compressive imaging

In this paper, we consider the reconstruction problem of hyperspectral images (HSIs) based on a new type of imaging system termed as coded aperture snapshot spectral imaging (CASSI). CASSI compresses multiple spectral bands of HSI into a single snapshot measurement which enjoys low cost, low bandwidth, and high-speed sensing rate. Despite the promising applications of CASSI systems, the bottleneck lies in the reconstruction algorithms. The existing CASSI reconstruction methods could not fully exploit the underlying structures of HSIs, and thereby demonstrate unsatisfactory reconstruction quality. To remedy this issue, we propose in this paper a novel tensor-based method by modeling two intrinsic HSI priors, namely nonlocal low-rank tensor train (NLTT) and 3-D weighted total variation (3DWTV). Taking into consideration that the snapshot measurement and targeted HSI should share similar spatial structures, we use the snapshot measurement to help group nonlocal spatially similar cubes within the unknown HSI to form 4-D tensors. And the NLTT prior employed on these 4-D tensors can effectively learn the correlations among the spatial, spectral and nonlocal modes thanks to the well-balanced matricization scheme of TT rank. Meanwhile, we employ the 3DWTV prior to characterize the local smoothness of the spatial and spectral modes. The resulting model can be solved efficiently by using the alternative direction method of multipliers (ADMM). Extensive experiments on both simulated and real CASSI datasets validate the improved reconstruction performance of our method over several other competing ones.

Unsupervised Spatial–Spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction

Hyperspectral compressive imaging takes advantage of compressive sensing theory to achieve coded aperture snapshot measurement without temporal scanning, and the entire 3-D spatial–spectral data is captured by a 2-D projection during a single integration period. Its core issue is how to reconstruct the underlying hyperspectral image (HSI) using compressive sensing reconstruction algorithms. Due to the diversity in the spectral response characteristics and wavelength range of different spectral imaging devices, previous works are often inadequate to capture complex spectral variations or lack the adaptive capacity to new hyperspectral imagers. In order to address these issues, we propose an unsupervised spatial–spectral network to reconstruct HSIs only from the compressive snapshot measurement. The proposed network acts as a conditional generative model conditioned on the snapshot measurement, and it exploits the spatial–spectral attention module to capture the joint spatial–spectral correlation of HSIs. The network parameters are optimized to make sure that the network output can closely match the given snapshot measurement according to the imaging model, thus the proposed network can adapt to different imaging settings, which can inherently enhance the applicability of the network. Extensive experiments upon multiple datasets demonstrate that our network can achieve better reconstruction results than the state-of-the-art methods.

Hyperspectral Snapshot Compressive Imaging With Dense Back-Projection Joint Attention Network

Hyperspectral Snapshot Compressive Imaging (SCI) system encodes three-dimensional hyperspectral images into a single two-dimensional snapshot measurement and then decodes the underlying 3D hyperspectral images by solving the compressive sensing reconstruction problem. Practical applications of SCI imaging systems require fast and high-quality reconstruction. To meet this requirement, we propose a novel encoder and decoder network with dense back-projection joint attention for hyperspectral SCI. The main contributions of our network lie in two aspects. First, we propose a dense back-projection module and deploy it in an encoder with five scales. It computes the back-projection between each scale and all its preceding scales, thereby fusing complementary information between different scales for efficient reconstruction. Second, we design a spatial-spectral attention module and deploy it in the decoder to boost reconstruction quality. By exploiting a cascade of spatial-spectral attention, it can efficiently capture spatial and spectral correlations in hyperspectral images with a low volume of parameters. In addition, a compound loss, including the reconstruction loss and the spatial-spectral total variation loss, is designed to guide network learning in an end-to-end manner. Intensive experiments on simulation and real data show that our method has obvious advantages over multiple state-of-the-art methods, achieving a significant improvement in reconstruction quality and a substantial reduction in running time.

Two-dimensional snapshot measurement of surface variation of anchoring in liquid crystal cells

ABSTRACT Enhanced, photoactive response can be realised by integrating liquid crystals with photoalignment or photoconductive alignment layers. Such cells, asymmetric by design, need to be monitored for the stability and uniformity, especially in the regions exposed to light. In this work, we report on an integrated, versatile model and technique to characterise their core parameters as well as more subtle effects, such as the strength of anchoring energy. The snapshot method also provides two-dimensional maps of the cells’ thickness, pretilt angle and uniformity.

Hyperspectral Snapshot Compressive Imaging with Non-Local Spatial-Spectral Residual Network

Snapshot Compressive Imaging is an emerging technology that is based on compressive sensing theory to achieve high-efficiency hyperspectral data acquisition. The core problem of this technology is how to reconstruct 3D hyperspectral data from the 2D snapshot measurement in a fast and high-quality manner. In this paper, we propose a novel deep network, which consists of the symmetric residual module and the non-local spatial-spectral attention module, to learn the reconstruction mapping in a data-driven way. The symmetric residual module uses symmetric residual connections to improve the potential of interaction between convolution operations and further promotes the fusion of local features. The non-local spatial-spectral attention module is designed to capture the non-local spatial-spectral correlation in the hyperspectral image. Specifically, this module calculates the channel attention matrix to capture the global correlations between all of the spectral channels, and it fuses the channel attention attained feature maps and the spatial attention weighted features as the module output, thus both of the spatial-spectral correlations of hyperspectral images can be fully utilized for reconstruction. In addition, a compound loss, including the reconstruction loss, the measurement loss, and the cosine loss, is designed to guide the end-to-end network learning. We experimentally evaluate the proposed method on simulation and real datasets. The experimental results show that the proposed network outperforms the competing methods in terms of the reconstruction quality and running time.

Compressive circular polarization snapshot spectral imaging

A compressive sensing based circular polarization snapshot spectral imaging system is proposed in this paper to acquire two-dimensional spatial, one-dimensional circular polarization (the right and left circular polarization), and one-dimensional spectral information by one snapshot measurement, simultaneously. The dispersed component combined with a double Amici prism and a Wollaston prism was used to implement the spectral shifting along two orthogonal directions. Theoretically, the spectral resolution could be improved compared with the traditional coded aperture snapshot spectral imager proposed by Brady et al. The right and left circular polarization components are extracted by the assembly with an achromatic quarter wave-plate and a Wollaston prism. The encoding and reconstruction are illustrated comprehensively. The feasibility and fidelity are verified from the aspects of image and spectral reconstruction evaluations. It provides us an alternative approach for circular polarization spectral imaging for the dynamic objects in the fields such as defogging, underwater imaging, insects detection, and so on.

Human ophthalmic artery as a sensor for non-invasive intracranial pressure monitoring: numerical modeling and in vivo pilot study

Intracranial pressure (ICP) monitoring is important in managing neurosurgical, neurological, and ophthalmological patients with open-angle glaucoma. Non-invasive two-depth transcranial Doppler (TCD) technique is used in a novel method for ICP snapshot measurement that has been previously investigated prospectively, and the results showed clinically acceptable accuracy and precision. The aim of this study was to investigate possibility of using the ophthalmic artery (OA) as a pressure sensor for continuous ICP monitoring. First, numerical modeling was done to investigate the possibility, and then a pilot clinical study was conducted to compare two-depth TCD-based non-invasive ICP monitoring data with readings from an invasive Codman ICP microsensor from patients with severe traumatic brain injury. The numerical modeling showed that the systematic error of non-invasive ICP monitoring was < 1.0 mmHg after eliminating the intraorbital and blood pressure gradient. In a clinical study, a total of 1928 paired data points were collected, and the extreme data points of measured differences between invasive and non-invasive ICP were − 3.94 and 4.68 mmHg (95% CI − 2.55 to 2.72). The total mean and SD were 0.086 ± 1.34 mmHg, and the correlation coefficient was 0.94. The results show that the OA can be used as a linear natural pressure sensor and that it could potentially be possible to monitor the ICP for up to 1 h without recalibration.

Snap-shot topography measurement via dual-VCSEL and dual wavelength digital holographic interferometry

In this paper, we propose a dual-wavelength digital holographic interferometry method based on a compact dual vertical-cavity surface-emitting laser (VCSEL) source. The source simultaneously emits light from two highly stabilized coherent light sources with slightly different wavelengths. A highly stabilized and adjustable current source enables the application of digital holographic dual-wavelength techniques to measure the shape of an object with height steps of a few millimeters. The wavelength drift over 12 h over the entire measurement range, which was evaluated using a wavemeter, was smaller than 1 pm. In addition to the low measurement uncertainty at large height jumps, the dual-wavelength digital holographic system distinguishes itself by its robustness to environmental disturbances such as air turbulence, heat load, and/or mechanical vibrations. This is enabled via a fiber-based almost common-path single-shot digital holographic acquisition of the information of the two different wavelengths using angular multiplexing. The experimental setup and data evaluation are discussed, and we present measurements of non-cooperative objects with specular reflective and/or diffuse reflective surfaces having different colors.

Lensless imaging of plant samples using the cross-polarized light.

Lensless imaging has recently become an alternative and cost-effective choice for many macro and micro applications, like wave-front sensing, fluorescence imaging, holographic microscopy, and so on. However, the polarized imaging, especially the cross-polarized light, has rarely been explored and integrated in lensless imaging methods. In this paper, we introduce the cross-polarized illumination into the lensless system for high-contrast and background-free imaging of plant samples. We capture a snapshot measurement and apply the blind deconvolution for reconstruction, obtaining the depolarized imaging of plant samples. Experiments exhibit the specific and sparse structures of the root system and vessel distribution of samples. We also build a corresponding lens-based system for performance comparison. This proposed lensless system is believed to have the potential in studies on the root development and water transport mechanism of plants in the future.

Tracer-based investigation of organic aerosols in marine atmospheres from marginal seas of China to the northwest Pacific Ocean

Abstract. We investigated the geographic distributions of organic tracers in total suspended particles over the marginal seas of China, including the Yellow and Bohai seas (YBS) and the South China Sea (SCS), and the northwest Pacific Ocean (NWPO) in spring, when Asian outflows strongly affect downwind marine atmospheres. The comparison of levoglucosan observed in this study with values from the literature showed that the concentrations of biomass burning aerosols over the NWPO increased largely in 2014. However more observations, in addition to our snapshot measurement, are need to confirm whether the large increase has occurred continuously over the last few decades. The increase led to a mean observed levoglucosan value (8.2±14 ng m−3) over the NWPO that was close to that over the SCS (9.6±8.6 ng m−3) and almost half of that over the YBS (21±11 ng m−3). Small geographic differences in monoterpene-derived and sesquiterpene-derived secondary organic tracer concentrations were obtained among the three atmospheres, although the causes may differ. By contrast, a large difference in isoprene-derived secondary organic tracer concentrations was observed among the three atmospheres, with the sum of tracer concentrations over the SCS (45±54 ng m−3) being several times and approximately 1 order of magnitude greater than that over the YBS (15±16 ng m−3) and the NWPO (2.3±1.6 ng m−3), respectively. The geographic distribution of aromatic-derived secondary organic tracers was similar to that of isoprene-derived secondary organic tracers, with a slightly narrower difference: 1.8±1.7, 1.1±1.4 and 0.3±0.5 ng m−3 over the SCS, the YBS and the NWPO, respectively. We discuss the causes of the distinctive geographic distributions of these tracers and present the tracer-based estimation of organic carbon.

A novel multi-magnetometer facility for on-ground characterization of spacecraft equipment

This paper aims to provide the research and development activities of ESA contract 4000111736/14/NL/GLC on Multi-Magnetometer Methods for Magnetic Dipole Modelling awarded to a consortium, consisting of EMTECH SPACE P.C. and National Technical University of Athens. In the frame of this activity, the rotational magnetic measurements of a unit under test are replaced by an increasing number of fixed magnetic field sensors, intended to perform a snap-shot measurement of its DC magnetic field. The scope of the research is to identify the most efficient number of magnetometers and their spatial geometry in order to effectively characterize magnetic sources consisting of several dipoles and quadrupoles. At later stages of the activity, the construction of a novel Multi-Magnetometer Facility was realized. The mechanical design of the facility is extensively illustrated and its capability to characterize magnetic field signatures is validated with a set of a-priori known magnetic sources.

Sparse DOA Estimation Algorithm Based on Fourth-Order Cumulants Vector Exploiting Restricted Non-Uniform Linear Array

Non-uniform linear array (NLA) has been widely used in the direction-of-arrival (DOA) estimation owing to its significant advantage of effectively expanding array aperture. In recent years, its related researches have been advanced due to the introduction of nested arrays and co-prime arrays. However, these NLAs require one or even many adjacent antennas whose spacing is the half wavelength of the incident source. In the practical application of the DOA estimation, if the frequency of the incident source is very high, the distances between adjacent antennas cannot reach half wavelength because of the existence of the actual antenna diameter. In this paper, a design method of the restricted NLA is proposed to solve this problem. First, a new NLA is designed by restricting the minimum spacing of adjacent elements, the set of fourth-order antenna spacing differences, and the maximum aperture of the array. Then, in order to solve the ambiguity problem in the DOA estimation caused by the spacing of adjacent antennas larger than half wavelength, we construct a single snapshot measurement sparse signal model by utilizing the fourth-order cumulants vector of the received signal after dimension reduction and by obtaining the estimation of DOA via a sparse reconstruction algorithm. Finally, compared with the minimum redundancy arrays, nested arrays, and co-prime arrays, the simulation experiments show that the proposed restricted NLAs have better performances of the DOA estimation.

Clustering Data Association Using Data Relevance in Spatial Domain for Doppler-Only RSN Localization

Localization of multitarget by means of a network of dispersed radar sensors using Doppler-only measurement faces the problem of data association, i.e., associating the measured Doppler-shifts with their originated targets. Conventionally, this association is realized based on correlations between successive estimations of targets' state. However, up to now, no data association method is available for localization using only one snapshot measurement. This paper tries to utilize the relevance of measurements in spatial domain instead of in time domain. An approach based on a clustering method, i.e., generalized fuzzy algorithmic scheme (GFAS), is introduced to solve the data association problem with a single snapshot. First, the measured data are formed as a matrix. Then, a clustering method is used for data association. This clustering is a procedure of collecting information of each target from the measurements of multiple sensors. Thus, the results of clustering will be used to associate the measured data with their originated targets. In order to overcome the problem that GFAS may associate more than one piece of data measured by the same sensor with a same target, an improved GFAS (IGFAS) is proposed. Different from other methods for data association, only one snapshot is required in the proposed method. Furthermore, the computational complexity will not increase exponentially as the number of targets or sensors increase. Simulation results validate the effectiveness of the proposed method.

High spatial sampling light-guide snapshot spectrometer.

A prototype fiber-based imaging spectrometer was developed to provide snapshot hyperspectral imaging tuned for biomedical applications. The system is designed for imaging in the visible spectral range from 400 to 700 nm for compatibility with molecular imaging applications as well as satellite and remote sensing. An 81 × 96 pixel spatial sampling density is achieved by using a custom-made fiber-optic bundle. The design considerations and fabrication aspects of the fiber bundle and imaging spectrometer are described in detail. Through the custom fiber bundle, the image of a scene of interest is collected and divided into discrete spatial groups, with spaces generated in between groups for spectral dispersion. This reorganized image is scaled down by an image taper for compatibility with following optical elements, dispersed by a prism, and is finally acquired by a CCD camera. To obtain an (x, y, λ) datacube from the snapshot measurement, a spectral calibration algorithm is executed for reconstruction of the spatial-spectral signatures of the observed scene. System characterization of throughput, resolution, and crosstalk was performed. Preliminary results illustrating changes in oxygen-saturation in an occluded human finger are presented to demonstrate the system's capabilities.

Snap-shot optical polarization spectroscopy using radially polarized pulses

By using radially polarized pulses for excitation in degenerate four-wave mixing (DFWM) spectroscopy, we realize highly sensitive snap-shot measurement of optical anisotropies in semiconductor films. Because the radially polarized pulses exhibit spatially varying linear polarizations, the optical anisotropies in samples can be evaluated from the spatial distributions of DFWM signals without rotating the polarization of the excitation pulses. We measure the excitons in GaN layers and evaluate the energies of uniaxial strain and the spin-exchange interaction constant. Our results prove both the accuracy and sensitivity of the snap-shot measurement.

Encrypted Three-dimensional Dynamic Imaging using Snapshot Time-of-flight Compressed Ultrafast Photography.

Compressed ultrafast photography (CUP), a computational imaging technique, is synchronized with short-pulsed laser illumination to enable dynamic three-dimensional (3D) imaging. By leveraging the time-of-flight (ToF) information of pulsed light backscattered by the object, ToF-CUP can reconstruct a volumetric image from a single camera snapshot. In addition, the approach unites the encryption of depth data with the compressed acquisition of 3D data in a single snapshot measurement, thereby allowing efficient and secure data storage and transmission. We demonstrated high-speed 3D videography of moving objects at up to 75 volumes per second. The ToF-CUP camera was applied to track the 3D position of a live comet goldfish. We have also imaged a moving object obscured by a scattering medium.

Optimization, tolerance analysis and implementation of a Stokes polarimeter based on the conical refraction phenomenon.

Recently, we introduced the basic concepts behind a new polarimeter device based on conical refraction (CR), which presents several appealing features compared to standard polarimeters. To name some of them, CR polarimeters retrieve the polarization state of an input light beam with a snapshot measurement, allow for substantially enhancing the data redundancy without increasing the measuring time, and avoid instrumental errors owing to rotating elements or phase-to-voltage calibration typical from dynamic devices. In this article, we present a comprehensive study of the optimization, robustness and parameters tolerance of CR based polarimeters. In addition, a particular CR based polarimetric architecture is experimentally implemented, and some concerns and recommendations are provided. Finally, the implemented polarimeter is experimentally tested by measuring different states of polarization, including fully and partially polarized light.

Novel Bio-Imaging Techniques by Harnessing Optical Highlighter Fluorescent Proteins

Optical highlighters are a remarkable family of fluorescent proteins (FPs) that could change their excitation and emission spectra upon certain wavelength illumination. Optical highlighters have been extensively used in super-resolution imaging, protein dynamics, gene expression and cellular trafficking. Herein we are harnessing these unique proteins in three different imaging applications: genetically-encoded microviscosity sensor, ultra-deep tissue imaging, and light-driven fluorescent timers. First, we report that the photoswitching kinetics of the chromophore inside Dronpa, a FP with the reversible on-off switching capability, is actually slowed down by increasing medium viscosity outside Dronpa. This effect is attributed to protein-flexibility mediated coupling where the chromophore's cis-trans isomerization is accompanied by conformational motion of protein beta-barrel. Based on this effect, we developed a genetically encoded protein-specific micro-viscosity sensor. Secondly, we have demonstrated a spectroscopy concept to extend the fundamental imaging-depth limit of multiphoton microscopy by generating super-nonlinearity of photo-switchable probes. Due to the long-lived nature of these switchable states, the nonlinearity effect could accumulate as the population is being cycled through these states. Conceptually different from conventional multiphoton processes mediated by transient virtual states, our strategy constitutes a new class of super-nonlinear fluorescence microscopy mediated by real population transfer. Last, we demonstrate a novel class of light-driven fluorescent timer (FT), based on the photo-convertible fluorescent proteins. It's a new method to image the protein “age” in live cells with a simple snap-shot measurement. This tunable light-driven FT will be a valuable tool that does not only provide the spatial location information of an individual protein, but also its temporal dynamics.

Communities of practice in the school workplace

PurposeThe first aim of this study is to explore to what extent communities of practice occur in the school workplace. The second aim is to explore the relation between communities of practice and diversity in composition of teacher teams.Design/methodology/approachQuantitative as well as qualitative data were gathered from seven teacher teams in a school for secondary education. Questionnaires and observation instruments were used to measure and analyze teacher teams' collaborative activities and diversity in composition.FindingsData suggest that communities of practice actually occur in the school workplace, but to a moderate extent. Also, communities of practice are related to four of the five diversity attributes that were investigated.Research limitations/implicationsThe current study is a snapshot measurement of communities of practice. Future research is recommended to focus on the development of communities of practice in the school workplace by including longitudinal measurements.Practical implicationsImplications for teachers and administrators include follow‐up activities aimed at stimulating and sustaining communities of practice as well as taking diversity considerations in team composition into account.Originality/valueStudies suggest that a community is a promising context for embedding collaboration into the culture of the school. However, empirical research that focuses on communities in the school workplace seems largely absent. This study provides insight in the occurrence of communities of practice from two perspectives: the perception of community members and the observation from outsiders.

Nonlinear dynamics of continuously measured Bose-Einstein condensates in one-dimensional harmonic traps

Continuous center-of-mass position measurements performed on an interacting harmonically trapped Bose-gas are considered. Using both semi-analytical mean-field approach and completely quantum numerical technique based on positive P-representation, it is demonstrated that the atomic delocalization due to the measurement back action is smaller for a strongly interacting gas. The numerically calculated second-order correlation functions demonstrate appearance of atomic bunching as a result of the center-of-mass measurement. Though being rather small the bunching is present also for strongly interacting gas which is in contrast with the case of unperturbed gas. The performed analysis allows to speculate that for relatively strong interactions the size of atomic cloud determined with a single snapshot measurement can become smaller than the ground-state cloud size.