Imaging Of Objects Research Articles

QCMOS Detectors and the Case of Hypothetical Primordial Black Holes in the Solar System, near Earth Objects, Transients, and Other High-cadence Observations

Abstract Recent progress with CMOS detector development has opened new parameter space for high cadence time resolved imaging of transients and fast proper motion solar system objects. Using computer simulations for a ground-based 1.23 m telescope, this research note illustrates the gain of a new generation of fast readout low noise qCMOS sensors over CCDs and makes the case for high precision monitoring of asteroid orbits that can potentially shed light on the hypothetical existence of low mass primordial black holes, as well as for other applications requiring high speed imaging.

Dose-efficient phase-contrast imaging of thick weak phase objects via OBF STEM using a pixelated detector.

Optimum bright-field scanning transmission electron microscopy (OBF STEM) is a recently developed low-dose imaging technique that uses a segmented or pixelated detector. While we previously reported that OBF STEM with a segmented detector has a higher efficiency than conventional STEM techniques such as annular bright field (ABF), the imaging efficiency is expected to be further improved by using a pixelated detector. In this study, we adopted a pixelated detector for the OBF technique and investigated the imaging characteristics. Because OBF imaging is based on the thick weak phase object approximation (tWPOA), a non-zero crystalline sample thickness is considered in addition to the conventional WPOA, where the pixelated OBF method can be regarded as the theoretical extension of single side band (SSB) ptychography. Thus, we compared these two techniques via signal-to-noise ratio transfer functions (SNRTFs), multi-slice image simulations, and experiments, showing how the OBF technique can improve dose efficiency from the conventional WPOA-based ptychographic imaging.

Stable Organic‐Inorganic Hybrid Sb(III) Halide Scintillator for Nonplanar Ultra‐Flexible X‐Ray Imaging

AbstractFlexible scintillator screens with excellent stability and low detection limits are crucial for X‐ray imaging applications. 0D organic metal halide materials have emerged as a strong contender in the scintillator fields, owing to their excellent optical characteristics and simple maneuverability. Herein, high‐quality and large quantities of C38H36P2Sb2Cl8 single crystals are synthesized through a simple solution approach. The prepared single crystals with dimer‐structure [Sb2Cl8]2− exhibit yellow emission with a near‐unity high photoluminescence quantum yield (PLQY) of 99.8%, and possess an exceptional light yield of 41300 photons MeV−1, and a detection limit as low as 45.6 nGyair s−1. On this basis, a large‐size and ultra‐flexible C38H36P2Sb2Cl8 scintillator utilized for X‐ray imaging is prepared by template assembled method, demonstrating a high spatial resolution of 8.15 lp mm−1. The prepared ultra‐flexible scintillator screen can achieve excellent X‐ray imaging even after multiple bending and stretching, which can also provide clear non‐planar X‐ray imaging for irregular objects. In addition, the scintillator shows excellent stability in light, heat, X‐ray irradiation, and water. These results not only expand the optoelectronic application field of organic‐inorganic hybrid antimony halides but also promote the rapid development of efficient ultra‐flexible scintillators.

Dark-field super-oscillatory microscopy with high-pass super-oscillatory filters

Super-resolution imaging of transparent objects is particularly challenging due to the interference caused by background light. This interference can significantly degrade image quality by reducing contrast and obscuring fine details. In this work, high-pass super-oscillatory masks are designed to address this issue by filtering out the background light on the Fourier plane. The resolution of the corresponding super-oscillatory point-spread function is 0.66 times the diffraction limit, enabling the resolution of transparent objects with 67% transparency at sub-wavelength scales. Additionally, the high-pass super-oscillatory masks are designed for a 1080 × 1920 liquid-crystal spatial light modulator, making future experimental implementation straightforward. This facilitates easy switching between bright-field and dark-field super-oscillatory microscopy, broadening the range of application scenarios for the system.

Survey of gravitationally lensed objects in HSC imaging (SuGOHI) – X. Strong lens finding in the HSC-SSP using convolutional neural networks

ABSTRACT We apply a novel model based on convolutional neural networks (CNN) to identify gravitationally lensed galaxies in multiband imaging of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) Survey. The trained model is applied to a parent sample of 2350 061 galaxies selected from the $\sim$ 800 deg$^2$ Wide area of the HSC-SSP Public Data Release 2. The galaxies in HSC Wide are selected based on stringent pre-selection criteria, such as multiband magnitudes, stellar mass, star formation rate, extendedness limit, photometric redshift range, etc. The trained CNN assigns a score from 0 to 1, with 1 representing lenses and 0 representing non-lenses. Initially, the CNN selects a total of 20 241 cutouts with a score greater than 0.9, but this number is subsequently reduced to 1522 cutouts after removing definite non-lenses for further visual inspection. We discover 43 grade A (definite) and 269 grade B (probable) strong lens candidates, of which 97 are completely new. In addition, we also discover 880 grade C (possible) lens candidates, 289 of which are known systems in the literature. We identify 143 candidates from the known systems of grade C that had higher confidence in previous searches. Our model can also recover 285 candidate galaxy-scale lenses from the Survey of Gravitationally lensed Objects in HSC Imaging (SuGOHI), where a single foreground galaxy acts as the deflector. Even though group-scale and cluster-scale lens systems are not included in the training, a sample of 32 SuGOHI-c (i.e. group/cluster-scale systems) lens candidates is retrieved. Our discoveries will be useful for ongoing and planned spectroscopic surveys, such as the Subaru Prime Focus Spectrograph project, to measure lens and source redshifts in order to enable detailed lens modelling.

Imaging of Live Cells by Digital Holographic Microscopy

Imaging of microscopic objects is of fundamental importance, especially in life sciences. Recent fast progress in electronic detection and control, numerical computation, and digital image processing, has been crucial in advancing modern microscopy. Digital holography is a new field in three-dimensional imaging. Digital reconstruction of a hologram offers the remarkable capability to refocus at different depths inside a transparent or semi-transparent object. Thus, this technique is very suitable for biological cell studies in vivo and could have many biomedical and biological applications. A comprehensive review of the research carried out in the area of digital holographic microscopy (DHM) for live-cell imaging is presented. The novel microscopic technique is non-destructive and label-free and offers unmatched imaging capabilities for biological and bio-medical applications. It is also suitable for imaging and modelling of key metabolic processes in living cells, microbial communities or multicellular plant tissues. Live-cell imaging by DHM allows investigation of the dynamic processes underlying the function and morphology of cells. Future applications of DHM can include real-time cell monitoring in response to clinically relevant compounds. The effect of drugs on migration, proliferation, and apoptosis of abnormal cells is an emerging field of this novel microscopic technique.

Error analysis based on a tunable wave plate polarization interferometric imaging spectrometer

Interference imaging spectroscopy combines modern imaging technology with spectral technology, holding significant importance for object imaging and spectral detection. This article introduces the principle of an adjustable wave plate polarization interferometric imaging spectrometer. The example design specifications are set for an observation wavelength range of 450–780 nm and a maximum resolution of 2 nm at 450 nm, with a 0.5 in detector as the base for calculating the specific dimensions of the Soleil–Babinet compensator. An investigation was conducted on the issues of nonuniform sampling, as well as three types of mechanical errors: flatness, wedge angle tolerance, and optical axis orientation accuracy. Emphasis was placed on discussing the impact of these errors on the instrument’s optical path difference and spectral reconstruction accuracy. This research provides theoretical guidance for the design and engineering of this miniaturized imaging spectrometer.

Multifunctional Intelligent Reconfigurable Metasurface.

The emergent reconfigurable metasurfaces (RMs) have attracted a lot of attention due to their potential in broad applications. As a general platform, RMs are able to control the reflection (or refraction) of incident waves with predefined functionalities. Nevertheless, the operation of RMs is highly dependent on the arrival direction of incidence. The self-adaptive design of an RM, so that it can respond to varied incident waves automatically, is highly requested in practical implementation, which is actually challenging. This study reports the realization of an intelligent RM (IRM) system, which can detect the arrival direction of impinging waves and respond to the incidence with a predefined functionality accordingly. This IRM system is constructed by integrating a direction of the arrival estimation module, a frontend by the varactor-based metasurface, and a central control unit. In experiments, an IRM system designed for TM polarization is demonstrated to perform various functions, i.e., retroreflection, directional reflection, and fixed-point energy focusing, which are highly requested by edge communication and sensing. The measured results imply that this IRM system responds quite well within a wide incident range from -60° to 60° in a frequency range from 9 to 9.5 GHz. The proposed IRM can be a good candidate for boosting 5G communication and Internet of Things applications, including beam shaping/steering, RCS manipulation, object imaging, and sensor recharging.

Speckle autocorrelation separation method based on frequency intensity for tracking moving object beyond optical memory effect

Abstract Imaging objects behind a scattering medium beyond the optical memory effect (OME) range is vital in various imaging scenarios. Despite numerous proposed imaging methods aimed at separating objects, effectively tracking moving objects remains challenging. In this paper, we present a non-invasive scattering imaging method to reconstruct target beyond the OME range and track moving objects using a speckle separation and position estimation technique. A speckle autocorrelation separation method based on frequency intensity is proposed to eliminate the noise from the cross-correlation and to separate the objects from the mixed speckles. A position estimation technique is proposed to determine the positional relationships of the objects using speckle deconvolution using the point spread function of imaging regions. A non-invasive imaging system is designed to track the moving object without prior knowledge of the shape and distribution of objects. Experimental validation shows the efficiency of the method for separation and localization, allowing for the reconstruction of the target behind the scattering medium and tracking of the moving object behind the scattering medium. Moreover, the method is capable of separating and tracking a moving object when multiple objects are in the background.

Deep diffusion learning of mutual-reflective structured light patterns for multi-body three-dimensional imaging

Simultaneous structured light imaging of multiple objects has become more demanding and widely in many scenarios involving robot operations in intelligent manufacturing. However, it is challenged by pattern aliasing caused by mutual reflection between high-reflective objects. To this end, we propose to learn clear fringe patterns from aliased mutual-reflective observations by diffusion models for achieving high-fidelity multi-body reconstruction in line with typical phase-shift algorithms. Regarding mutual reflection imaging as a formation of adding significant noise, we build a supervised generative learning framework based on diffusion models and then train a self-attention-based deep network with a U-Net-like skip-connected encoder-decoder architecture. We demonstrate the generalization capability of the trained model in fringe pattern recovery and its performance in phase and three-dimensional (3D) shape reconstruction. Both experimental results show that the proposed method has the expected feasibility and accuracy, heralding a promising solution for addressing the current challenge in various multi-body mutual-reflective 3D reconstruction tasks.

Efficient metal free organic radical scintillators

The development of high-performance metal-free organic X-ray scintillators (OXSTs), characterized by a synergistic combination of robust X-ray absorption, efficient exciton utilization, and short luminescence lifetimes, poses a considerable challenge. Here we present an effective strategy for achieving augmented X-ray scintillation through the utilization of halogenated open-shell organic radical scintillators. Our experimental results demonstrate that the synthesized scintillators exhibit strong X-ray absorption derived from halogen atoms, display efficacious X-ray stability, and theoretically achieve 100% exciton utilization efficiency with a short lifetime (∼18 ns) due to spin-allowed doublet transitions. The superior X-ray scintillation performance exhibited by these organic radicals is not only exploitable in X-ray radiography for contrast imaging of various objects but also applicable in a medical high-resolution micro-computer-tomography system for the clear visualization of fibrous veins within a bamboo stick. Our study substantiates the promise of organic radicals as prospective candidates for OXSTs, offering valuable insights and a roadmap for the development of advanced organic radical scintillators geared towards achieving high-quality X-ray radiography.

Multifocal microscopy for functional imaging of neural systems.

Rapid acquisition of large imaging volumes with microscopic resolution is an essential unmet need in biological research, especially for monitoring rapid dynamical processes such as fast activity in distributed neural systems. We present a multifocal strategy for fast, volumetric, diffraction-limited resolution imaging over relatively large and scalable fields of view (FOV) using single-camera exposures. Our multifocal microscopy approach leverages diffraction to image multiple focal depths simultaneously. It is based on a custom-designed diffractive optical element suited to low magnification and large FOV applications and customized prisms for chromatic correction, allowing for wide bandwidth fluorescence imaging. We integrate this system within a conventional microscope and demonstrate that our design can be used flexibly with a variety of magnification/numerical aperture (NA) objectives. We first experimentally and numerically validate this system for large FOV microscope imaging (three orders-of-magnitude larger volumes than previously shown) at resolutions compatible with cellular imaging. We then demonstrate the utility of this approach by visualizing high resolution three-dimensional (3D) distributed neural network at volume rates up to 100Hz. These demonstrations use genetically encoded indicators to measure functional neural imaging both in vitro and in vivo. Finally, we explore its potential in other important applications, including blood flow visualization and real-time, microscopic, volumetric rendering. Our study demonstrates the advantage of diffraction-based multifocal imaging techniques for 3D imaging of mm-scale objects from a single-camera exposure, with important applications in functional neural imaging and other areas benefiting from volumetric imaging.

Maritime ISAR detection and tracking algorithm for multiple maneuvering extended vessels in heavy-tailed clutter using SK-MM-Sub-RMM-MB-TBD filter

Multiple extended objects tracking (EOT) tasks play an important role in computer vision and engineering applications of artificial intelligence. In particular, nonlinear marine object imaging and tracking has received an increasing amount of attention due to its enormous application potential in the field of marine engineering, Autonomous Underwater Vehicles, and Remotely Operated Vehicles. Under high sea state, ship-EOTs perform complex maneuvering movements due to strong disturbances such as sea winds and sea waves. In this paper, we exploit emergent maneuvering EOTs (M-EOTs) methodologies in heavy-tailed clutter. We propose a M-EOT procedure in real-time scenario based on the popular multi-Bernoulli (MB)-TBD filter in maritime inverse synthetic aperture radar (ISAR) systems, and in particular, we describe the extended ship target state through the random matrices model (RMM). In RMM, scatter centers are distributed symmetrically around the M-EOT's centroid. However, in ship M-EOT scenario, the distribution over the whole object is not symmetrical, but distributed and skewed in some portions while a target maneuvers. To solve this problem, a novel robustness observation model is represented by using skewed (SK) non-symmetrically normal distribution and multiple model (MM) MB-TBD with more than one ellipse. Simulation and experimental results illustrate that the proposed SK-MM-Sub-RMM-MB-TBD filter outperforms the existing filters for M-EOTs.

Broadband infrared imaging governed by guided-mode resonance in dielectric metasurfaces

Nonlinear metasurfaces have experienced rapid growth recently due to their potential in various applications, including infrared imaging and spectroscopy. However, due to the low conversion efficiencies of metasurfaces, several strategies have been adopted to enhance their performances, including employing resonances at signal or nonlinear emission wavelengths. This strategy results in a narrow operational band of the nonlinear metasurfaces, which has bottlenecked many applications, including nonlinear holography, image encoding, and nonlinear metalenses. Here, we overcome this issue by introducing a new nonlinear imaging platform utilizing a pump beam to enhance signal conversion through four-wave mixing (FWM), whereby the metasurface is resonant at the pump wavelength rather than the signal or nonlinear emissions. As a result, we demonstrate broadband nonlinear imaging for arbitrary objects using metasurfaces. A silicon disk-on-slab metasurface is introduced with an excitable guided-mode resonance at the pump wavelength. This enabled direct conversion of a broad IR image ranging from >1000 to 4000 nm into visible. Importantly, adopting FWM substantially reduces the dependence on high-power signal inputs or resonant features at the signal beam of nonlinear imaging by utilizing the quadratic relationship between the pump beam intensity and the signal conversion efficiency. Our results, therefore, unlock the potential for broadband infrared imaging capabilities with metasurfaces, making a promising advancement for next-generation all-optical infrared imaging techniques with chip-scale photonic devices.

Computing imaging in shortwave infrared bands enabled by MoTe2/Si 2D‐3D heterojunction‐based photodiode

AbstractBreakthroughs brought about by two‐dimensional (2D) materials in the field of photodetection have opened up new possibilities in infrared imaging. However, challenges still exist in fabricating high‐density detector arrays using such materials, which are essential for traditional imaging systems. In this study, we present a state‐of‐the‐art computing imaging system that utilizes a MoTe2/Si self‐powered photodetector coupled with flexible Hadamard modulation algorithms. This system demonstrates remarkable capability to produce high‐quality images in the shortwave infrared (SWIR) band, surpassing the capabilities of devices based on alternative material systems. The exceptional infrared imaging capability primarily stems from the MoTe2/Si photodetector's inherent features, including an ultra‐wide spectral range (265–1550 nm) and extremely high sensitivity (linear dynamic range (LDR) up to 123 dB, responsivity (R) up to 0.33 A W–1, external quantum efficiency (EQE) up to 43% and a specific detectivity (D*) exceeding 2.9 × 1011 Jones). Moreover, the imaging system demonstrates the ability to achieve high‐quality edge imaging of objects in the SWIR band (1550 nm), even in strong scattering environments and under low sampling rate conditions (sampling rate of 25%). We believe that this work will effectively advance the application scope of 2D materials in the field of computational imaging in SWIR bands.image

Depth-resolved imaging through scattering media based on modified phasor field diffraction

The imaging through scattering media has significant applications in fields, including rescue and security. However, the phasor field algorithm ignores the effect of the additional optical path introduced by the scattering medium for the imaging of the hidden objects behind scattering media. To improve the accuracy, a modified phasor field diffraction method combined with the spatial average path for depth-resolved imaging through scattering media is proposed here. The additional optical path introduced by the scattering medium is resolved through the spatial average path, while the phasor field describes the free-space propagation of the light in air. We verify the feasibility and robustness of the proposed method on public transient data, real scenes and synthetic data. The results demonstrate that the higher-quality results with the proposed method are obtained, and the accurate depth information of objects is provided compared with the phasor field, as evidenced by evaluating the mean depth. This method has potential applications in medical imaging, remote sensing and other fields.

Learning-based real-time imaging through dynamic scattering media

Imaging through dynamic scattering media is one of the most challenging yet fascinating problems in optics, with applications spanning from biological detection to remote sensing. In this study, we propose a comprehensive learning-based technique that facilitates real-time, non-invasive, incoherent imaging of real-world objects through dense and dynamic scattering media. We conduct extensive experiments, demonstrating the capability of our technique to see through turbid water and natural fog. The experimental results indicate that the proposed technique surpasses existing approaches in numerous aspects and holds significant potential for imaging applications across a broad spectrum of disciplines.

Meta Shack–Hartmann wavefront sensor with large sampling density and large angular field of view: phase imaging of complex objects

Shack–Hartmann wavefront sensors measure the local slopes of an incoming wavefront based on the displacement of focal spots created by a lenslet array, serving as key components for adaptive optics for astronomical and biomedical imaging. Traditionally, the challenges in increasing the density and the curvature of the lenslet have limited the use of such wavefront sensors in characterizing slowly varying wavefront structures. Here, we develop a metasurface-enhanced Shack–Hartmann wavefront sensor (meta SHWFS) to break this limit, considering the interplay between the lenslet parameters and the performance of SHWFS. We experimentally validate the meta SHWFS with a sampling density of 5963 per mm2 and a maximum acceptance angle of 8° which outperforms the traditional SFWFS by an order of magnitude. Furthermore, to the best of our knowledge, we demonstrate the first use of a wavefront sensing scheme in single-shot phase imaging of highly complex patterns, including biological tissue patterns. The proposed approach opens up new opportunities in incorporating exceptional light manipulation capabilities of the metasurface platform in complex wavefront characterization.

A Convolutional Neural Network with Multifrequency and Structural Similarity Loss Functions for Electromagnetic Imaging.

In this paper, artificial intelligence (AI) technology is applied to the electromagnetic imaging of anisotropic objects. Advances in magnetic anomaly sensing systems and electromagnetic imaging use electromagnetic principles to detect and characterize subsurface or hidden objects. We use measured multifrequency scattered fields to calculate the initial dielectric constant distribution of anisotropic objects through the backpropagation scheme (BPS). Later, the estimated multifrequency permittivity distribution is input to a convolutional neural network (CNN) for the adaptive moment estimation (ADAM) method to reconstruct a more accurate image. In the meantime, we also improve the definition of loss function in the CNN. Numerical results show that the improved loss function unifying the structural similarity index measure (SSIM) and root mean square error (RMSE) can effectively enhance image quality. In our simulation environment, noise interference is considered for both TE (transverse electric) and TM (transverse magnetic) waves to reconstruct anisotropic scatterers. Lastly, we conclude that multifrequency reconstructions are more stable and precise than single-frequency reconstructions.

Optimization-based approach for high-fidelity phase retrieval from sparse interferometric data: implications for industry and biological research

Optical profilometers provide a non-contact, non-destructive method for swiftly profiling 3D surfaces. White light interferometers, often used for this purpose, employ a 5-phase shifting technique for precise phase maps. However, capturing multiple frames introduces mechanical movement, which impedes imaging of dynamic objects. White light’s low spatial-temporal coherence mitigates speckles and spurious fringes while offering high axial resolution. Creating a high fringe density interferogram with low-coherence light is challenging. Introducing a tilt angle in the interferometer can increase the fringe density, which is still insufficient for phase map retrieval using the single-shot Fourier transform method. We propose an adaptive optimization framework to recover phase maps from single low fringe density interferograms. This method iteratively extracts reference beam information, eliminating mechanical movement and enhancing system stability while reducing costs and system bulkiness. The simulation and experimental results on a step-phase object (etched on silicon) and biological MG63 osteosarcoma cells validate the efficacy of a single-shot optimization scheme. For comparison, the phase maps of the same objects were obtained using the single-shot Fourier transform and multi-shot 5-phase shifted methods. The single-shot optimization technique shows efficient performance, yielding phase maps with reasonable accuracy, potentially replacing the 5-phase shifting technique in industrial and biological diagnostics.