Search for: imaging sensor

Polarimetric imaging has a wide range of applications for uncovering features invisible to human eyes and conventional imaging sensors. Chip-integrated, fast, cost-effective, and accurate full-Stokes polarimetric imaging sensors are highly desirable in many applications, which, however, remain elusive due to fundamental material limitations. Here we present a chip-integratedMetasurface-based Full-StokesPolarimetricImaging sensor (MetaPolarIm) realized by integrating an ultrathin (~600 nm) metasurface polarization filter array (MPFA) onto a visible imaging sensor with CMOS compatible fabrication processes. The MPFA is featured with broadband dielectric-metal hybrid chiral metasurfaces and double-layer nanograting polarizers. This chip-integrated polarimetric imaging sensor enables single-shot full-Stokes imaging (speed limited by the CMOS imager) with the most compact form factor, records high measurement accuracy, dual-color operation (green and red) and a field of view up to 40 degrees. MetaPolarIm holds great promise to enable transformative applications in autonomous vision, industry inspection, space exploration, medical imaging and diagnosis.

 

In this paper, we assess the noise-susceptibility of coherent macroscopic single random phase encoding (SRPE) lensless imaging by analyzing how much information is lost due to the presence of camera noise. We have used numerical simulation to first obtain the noise-free point spread function (PSF) of a diffuser-based SRPE system. Afterwards, we generated a noisy PSF by introducing shot noise, read noise and quantization noise as seen in a real-world camera. Then, we used various statistical measures to look at how the shared information content between the noise-free and noisy PSF is affected as the camera-noise becomes stronger. We have run identical simulations by replacing the diffuser in the lensless SRPE imaging system with lenses for comparison with lens-based imaging. Our results show that SRPE lensless imaging systems are better at retaining information between corresponding noisy and noiseless PSFs under high camera noise than lens-based imaging systems. We have also looked at how physical parameters of diffusers such as feature size and feature height variation affect the noise robustness of an SRPE system. To the best of our knowledge, this is the first report to investigate noise robustness of SRPE systems as a function of diffuser parameters and paves the way for the use of lensless SRPE systems to improve imaging in the presence of image sensor noise.

 

The direct imaging of an Earth-like exoplanet will require sub-nanometric wave-front control across large light-collecting apertures to reject host starlight and detect the faint planetary signal. Current adaptive optics systems, which use wave-front sensors that reimage the telescope pupil, face two challenges that prevent this level of control: non-common-path aberrations, caused by differences between the sensing and science arms of the instrument; and petaling modes: discontinuous phase aberrations caused by pupil fragmentation, especially relevant for the upcoming 30 m class telescopes. Such aberrations drastically impact the capabilities of high-contrast instruments. To address these issues, we can add a second-stage wave-front sensor to the science focal plane. One promising architecture uses the photonic lantern (PL): a waveguide that efficiently couples aberrated light into single-mode fibers (SMFs). In turn, SMF-confined light can be stably injected into high-resolution spectrographs, enabling direct exoplanet characterization and precision radial velocity measurements; simultaneously, the PL can be used for focal-plane wave-front sensing. We present a real-time experimental demonstration of the PL wave-front sensor on the Subaru/SCExAO testbed. Our system is stable out to around ±400 nm of low-order Zernike wave-front error and can correct petaling modes. When injecting ∼30 nm rms of low-order time-varying error, we achieve ∼10× rejection at 1 s timescales; further refinements to the control law and lantern fabrication process should make sub-nanometric wave-front control possible. In the future, novel sensors like the PL wave-front sensor may prove to be critical in resolving the wave-front control challenges posed by exoplanet direct imaging.

 

Fluorine magnetic resonance imaging (¹⁹F MRI) has emerged as an attractive alternative to conventional¹H MRI due to enhanced specificity deriving from negligible background signal in this modality. We report a dual nanoparticle conjugate (DNC) platform as an aptamer‐based sensor for use in¹⁹F MRI.DNCconsists of core–shell nanoparticles with a liquid perfluorocarbon core and a mesoporous silica shell (¹⁹F‐MSNs), which give a robust¹⁹F MR signal, and superparamagnetic iron oxide nanoparticles (SPIONs) as magnetic quenchers. Due to the strong magnetic quenching effects of SPIONs, this platform is uniquely sensitive and functions with a low concentration of SPIONs (4 equivalents) relative to¹⁹F‐MSNs. The probe functions as a “turn‐on” sensor using target‐induced dissociation of DNA aptamers. The thrombin binding aptamer was incorporated as a proof‐of‐concept (DNC_Thr), and we demonstrate a significant increase in¹⁹F MR signal intensity whenDNC_Thris incubated with human α‐thrombin. This proof‐of‐concept probe is highly versatile and can be adapted to sense ATP and kanamycin as well. Importantly,DNC_Thrgenerates a robust¹⁹F MRI “hot‐spot” signal in response to thrombin in live mice, establishing this platform as a practical, versatile, and biologically relevant molecular imaging probe.

 

Fluorine magnetic resonance imaging (¹⁹F MRI) has emerged as an attractive alternative to conventional¹H MRI due to enhanced specificity deriving from negligible background signal in this modality. We report a dual nanoparticle conjugate (DNC) platform as an aptamer‐based sensor for use in¹⁹F MRI.DNCconsists of core–shell nanoparticles with a liquid perfluorocarbon core and a mesoporous silica shell (¹⁹F‐MSNs), which give a robust¹⁹F MR signal, and superparamagnetic iron oxide nanoparticles (SPIONs) as magnetic quenchers. Due to the strong magnetic quenching effects of SPIONs, this platform is uniquely sensitive and functions with a low concentration of SPIONs (4 equivalents) relative to¹⁹F‐MSNs. The probe functions as a “turn‐on” sensor using target‐induced dissociation of DNA aptamers. The thrombin binding aptamer was incorporated as a proof‐of‐concept (DNC_Thr), and we demonstrate a significant increase in¹⁹F MR signal intensity whenDNC_Thris incubated with human α‐thrombin. This proof‐of‐concept probe is highly versatile and can be adapted to sense ATP and kanamycin as well. Importantly,DNC_Thrgenerates a robust¹⁹F MRI “hot‐spot” signal in response to thrombin in live mice, establishing this platform as a practical, versatile, and biologically relevant molecular imaging probe.

 

Squeezed light has long been used to enhance the precision of a single optomechanical sensor. An emerging set of proposals seeks to use arrays of optomechanical sensors to detect weak distributed forces, for applications ranging from gravity-based subterranean imaging to dark matter searches; however, a detailed investigation into the quantum-enhancement of this approach remains outstanding. Here, we propose an array of entanglement-enhanced optomechanical sensors to improve the broadband sensitivity of distributed force sensing. By coherently operating the optomechanical sensor array and distributing squeezing to entangle the optical fields, the array of sensors has a scaling advantage over independent sensors (i.e.,$$\sqrt{M}\to M$$$\sqrt{M}\to M$, whereMis the number of sensors) due to coherence as well as joint noise suppression due to multi-partite entanglement. As an illustration, we consider entanglement-enhancement of an optomechanical accelerometer array to search for dark matter, and elucidate the challenge of realizing a quantum advantage in this context.

 

We present a direct imaging study of V892 Tau, a young Herbig Ae/Be star with a close-in stellar companion and circumbinary disk. Our observations consist of images acquired via Keck II/NIRC2 with nonredundant masking and the pyramid wavefront sensor at$K\prime$band (2.12μm) and$L\prime$band (3.78μm). Sensitivity to low-mass accreting companions and cool disk material is high at$L\prime$band, while complimentary observations at$K\prime$band probe hotter material with higher angular resolution. These multiwavelength, multiepoch data allow us to differentiate the secondary stellar emission from disk emission and deeply probe the structure of the circumbinary disk at small angular separations. We constrain architectural properties of the system by fitting geometric disk and companion models to the$K\prime$- and$L\prime$-band data. From these models, we constrain the astrometric and photometric properties of the stellar binary and update the orbit, placing the tightest estimates to date on the V892 Tau orbital parameters. We also constrain the geometric structure of the circumbinary disk, and resolve a circumprimary disk for the first time.

 

Fluorescence and, more generally, photoluminescence enable high contrast imaging of targeted regions of interest through the use of photoluminescent probes with high specificity for different targets. Fluorescence can be used for rare cell imaging; however, this often requires a high space-bandwidth product: simultaneous high resolution and large field of view. With bulky traditional microscopes, high space-bandwidth product images require time-consuming mechanical scanning and stitching. Lensfree imaging can compactly and cost-effectively achieve a high space-bandwidth product in a single image through computational reconstruction of images from diffraction patterns recorded over the full field of view of standard image sensors. Many methods of lensfree photoluminescent imaging exist, where the excitation light is filtered before the image sensor, often by placing spectral filters between the sample and sensor. However, the sample-to-sensor distance is one of the limiting factors on resolution in lensfree systems and so more competitive performance can be obtained if this distance is reduced. Here, we show a time-gated lensfree photoluminescent imaging system that can achieve a resolution of 8.77 µm. We use europium chelate fluorophores because of their long lifetime (642 µs) and trigger camera exposure ∼50 µs after excitation. Because the excitation light is filtered temporally, there is no need for physical filters, enabling reduced sample-to-sensor distances and higher resolutions. 

Tactile imaging sensor determines the tumor's mechanical properties such as size, depth, and Young's modulus based on the principle of total internal reflection of light. To improve the classifying accuracy of the Tactile imaging sensor, we introduce ultrasound signals and estimate the difference in the tumor tactile images. A developed vibro-acoustic tactile imaging sensor was used to classify benign and malignant tumors. We test the developed system on breast tumor phantoms. These vibrated tactile images are analyzed to improve the overall performance of tumor detection. 

Single-photon 3D cameras can record the time of arrival of billions of photons per second with picosecond accuracy. One common approach to summarize the photon data stream is to build a per-pixel timestamp histogram, resulting in a 3D histogram tensor that encodes distances along the time axis. As the spatio-temporal resolution of the histogram tensor increases, the in-pixel memory requirements and output data rates can quickly become impractical. To overcome this limitation, we propose a family of linear compressive representations of histogram tensors that can be computed efficiently, in an online fashion, as a matrix operation. We design practical lightweight compressive representations that are amenable to an in-pixel implementation and consider the spatio-temporal information of each timestamp. Furthermore, we implement our proposed framework as the first layer of a neural network, which enables the joint end-to-end optimization of the compressive representations and a downstream SPAD data processing model. We find that a well-designed compressive representation can reduce in-sensor memory and data rates up to 2 orders of magnitude without significantly reducing 3D imaging quality. Finally, we analyze the power consumption implications through an on-chip implementation.