Array Camera Research Articles

Jaguar density estimation in Mexico: The conservation importance of considering home range orientation in spatial capture–recapture

AbstractAccurate estimation of population parameters for imperiled wildlife is crucial for effective conservation decision‐making. Population density is commonly used for monitoring imperiled species across space and time, and spatial capture–recapture (SCR) models can produce unbiased density estimates. However, many imperiled species are restricted to fragmented remnant habitats in landscapes severely modified by humans, which can alter animal space use in ways that violate typical SCR model assumptions, possibly cryptically biasing density estimates and misinforming conservation actions. Using data from a two‐year camera‐trapping survey in the Central Pacific Coast region, Mexico, we demonstrate the potential importance to endangered jaguar (Panthera onca) conservation of considering non‐circular home ranges when estimating population density with SCR. Strong evidence existed that jaguars had elliptical home ranges wherein movements primarily occurred along linearly arranged coastal habitats that the camera array aligned with. Accounting for this movement with the SCR anisotropic detection function transformation, density estimates were 30%–32% higher than estimates from standard SCR models that assumed circular home ranges. Given much of suitable jaguar habitat in Mexico is fragmented and linearly oriented along coastlines and mountain ranges, accommodating irregular space use in SCR may be critical for obtaining reliable density estimates to inform effective jaguar conservation.

Debris disks around M dwarfs: The Herschel DEBRIS survey

The Herschel open-time key program Disc Emission via a Bias-free Reconnaissance in the Infrared and Sub-millimeter (DEBRIS) is an unbiased survey of the nearest ∼ 100 stars for each stellar type A-M observed with a uniform photometric sensitivity to search for cold debris disks around them. The analysis of the Photoconductor Array Camera and Spectrometer (PACS) photometric observations of the 94 DEBRIS M dwarfs of this program is presented in this paper, following upon two companion papers on the DEBRIS A-star and FGK-star subsamples. In the M-dwarf subsample, two debris disks have been detected, around the M3V dwarf GJ 581 and the M4V dwarf Fomalhaut C (LP 876-10). This result gives a disk detection rate of $2.1^ $ % at the 68% confidence level, significantly less than measured for earlier stellar types in the DEBRIS program. However, we show that the survey of the DEBRIS M-dwarf subsample is about ten times shallower than the surveys of the DEBRIS FGK subsamples when studied in the physical parameter space of the disk's fractional dust luminosity versus blackbody radius. Furthermore, had the DEBRIS K-star subsample been observed at the same shallower depth in this parameter space, its measured disk detection rate would have been statistically consistent with the one found for the M-dwarf subsample. Hence, the incidence of debris disks does not appear to drop from the K subsample to the M subsample of the DEBRIS program, when considering disks in the same region of physical parameter space. An alternative explanation is that the only two bright disks discovered in the M-dwarf subsample would not, in fact, be statistically representative of the whole population.

Compressive light field photography with high spatial-angular resolution based on single-pixel imaging

Compressive light field photography with high spatial-angular resolution based on single-pixel imaging

Geometric Calibration for the Linear Array Camera Based on Star Observation

Geometric Calibration for the Linear Array Camera Based on Star Observation

KALYPSO LGAD — a MHz repetition rate line camera based on trenchisolated low gain avalanche detector

Designed for accelerator beam diagnostics and photon science applications, KALYPSO is a line array camera that stands out for its high-speed performance with the ability to operate at rates up to 12 Mfps in continuous readout mode while maintaining full occupancy. In this contribution, the KALYPSO system with sensor based on TI-LGAD is presented. The latest version of this system is employed as a beam diagnostic imaging sensor to measure radiation profiles of the particle beam at the KIT accelerator, KARA. The system's key features will be presented, including its linearity, sensitivity, and dynamic range.

Recording dynamic facial micro-expressions with a multi-focus camera array

Recording dynamic facial micro-expressions with a multi-focus camera array

Blind aberration correction for light field photography

Aberration correction is critical for obtaining sharp images but remains a challenging task. Owing to its ability to record both spatial and angular information of light rays, light field imaging is a powerful method to measure and correct optical aberrations. However, current methods need extensive calibrations to obtain prior information about the camera, which is restrictive in real-world applications. In this work, we propose a two-stage blind aberration correction method for light field imaging, which leverages self-supervised learning for general blind aberration correction and low-rank approximation to exploit the specific correlations of light fields to further abate aberrations. We demonstrated experimentally the superiority of our method over current state-of-the-art.

Super-Resolved Fluorescence Lifetime Imaging of Single Cy3 Molecules and Quantum Dots Using Time-Correlated Single Photon Counting with a Four-Pixel Fiber Optic Array Camera.

Time-resolved single molecule localization microscopy (TR-SMLM) with a 2 × 2 pixel fiber optic array camera was combined with time-correlated single photon counting (TCSPC) to obtain super-resolved fluorescence lifetime images of individual Cy3 dye molecules and individual colloidal CdSe/CdS/ZnS core/shell/shell semiconductor quantum dots (QDs). The characteristic blinking and bleaching behavior of the Cy3 and the blinking behavior of the QD emitters were used as distinguishing optical characteristics to isolate them and determine their centroid locations with spatial resolution below the optical diffraction limit. TCSPC was used to characterize the fluorescence lifetime and intensity corresponding to each emitter location. The mean centroid locations of the QDs could be determined with a precision of ∼1-4 nm, and the mean centroid locations of the Cy3 molecules could be determined with a precision of ∼2-9 nm, depending on the number of photons collected during the observation time. In a super-resolved fluorescence lifetime image with a single Cy3 dye molecule and a single QD separated by ∼34 nm, the two emitters were distinguished based on the average photon arrival times with respect to the excitation laser pulse observed during time intervals when only one emitter was in the on state, ∼6 ns for Cy3 and ∼17 ns for the QD. The mean distance between the two emitters was determined with a precision of ∼8 nm. The feasibility of using this super-resolved fluorescence lifetime imaging technique to investigate QD-dye complexes that use Förster resonance energy transfer (FRET) and/or electron transfer to form optical biosensors is discussed.

Using polarization to estimate surface normals at air–water interfaces for correction of refraction in seafloor imaging

The retrieval of sea surface normal vectors using shape-from-polarization is investigated for the purpose of correcting for refraction at the water–air interface when imaging from above the water. In shallow clear water and overcast conditions, spectral longpass filtering (using a hard-coated 850 nm cut-on wavelength filter) is demonstrated to 1) avoid artifacts from the ground in the measured polarization state, and 2) reduce polarization from water-leaving radiance sufficiently to derive shape information exclusively from the polarization produced by specular reflection. The dependence of the method on meteorological conditions is studied. Measurements are performed with a commercial polarization filter array (PFA) camera. Due to the decreasing PFA efficiency towards the near-infrared, rigorous characterization and calibration measurements were performed and recommendations (e.g., on the f-number) elaborated. Overcoming the paraxial approximation, normal vectors are then retrieved with systematic errors of 0.1∘ (image center) to 0.5∘−0.8∘ (edges/corners) for a flat water surface. An image of the sea floor corrected for surface refraction shows maximum displacements of 10–20 pixels only (corresponding to 0.25∘) with respect to a validation image without water.

Dynamic light field reconstruction via densely connected deep equilibrium model

High-resolution consumer plenoptic cameras usually feature low frame rates, making them not well-suited for capturing high-speed motion scenes. To compensate for this limitation, we extend the original snapshot compressive imaging system to plenoptic cameras and propose a densely connected deep equilibrium (DEQ) model for high-quality dynamic light field (LF) reconstruction, abbreviated as DLFDEQ. Specifically, we perform temporal compression encoding on a dynamic LF and model the reconstruction process as an inverse problem with an implicit regularization term. To solve this inverse problem, we present a densely connected DEQ model based on gradient descent. Our approach demonstrates stronger robustness and better detail retention than existing methods. We can practically quadruple the original camera’s frame rate by continually capturing and retrieving these measurement frames with high reconstruction accuracy.

Improving Sewer Damage Inspection: Development of a Deep Learning Integration Concept for a Multi-Sensor System.

The maintenance and inspection of sewer pipes are essential to urban infrastructure but remain predominantly manual, resource-intensive, and prone to human error. Advancements in artificial intelligence (AI) and computer vision offer significant potential to automate sewer inspections, improving reliability and reducing costs. However, the existing vision-based inspection robots fail to provide data quality sufficient for training reliable deep learning (DL) models. To address these limitations, we propose a novel multi-sensor robotic system coupled with a DL integration concept. Following a comprehensive review of the current 2D (image) and 3D (point cloud) sewage pipe inspection methods, we identify key limitations and propose a system incorporating a camera array, front camera, and LiDAR sensor to optimise surface capture and enhance data quality. Damage types are assigned to the sensor best suited for their detection and quantification, while tailored DL models are proposed for each sensor type to maximise performance. This approach enables the optimal detection and processing of relevant damage types, achieving higher accuracy for each compared to single-sensor systems.

The path-tracing simulation of light-field camera system: SurfCam/GrainCams for lunar surface exploration

The path-tracing simulation of light-field camera system: SurfCam/GrainCams for lunar surface exploration

Observations of Ruapehu Crater Lake (Te Wai ā-moe) and implications for lake dynamics and volcano monitoring

All historical eruptions at Ruapehu have occurred from its Crater Lake, Te Wai ā-moe. This study aims to better understand Crater Lake dynamics by using visible light and long wavelength infrared images of the lake. Over 10,000 images from 1902 – 2021 were analysed to produce a time-series of lake observations. Our results show that visible light observations reveal colour changes on the entire Crater Lake surface from blue to grey, and localised grey, yellow, and black discolourations. Grey discolourations are interpreted as localised upwellings of lake-floor sediment, and yellow and black material to comprise vent-hosted sulphur/sulphides, both transported by volcanic fluids from subaqueous vents to the surface. The locations of upwellings were used to identify five vent locations beneath Crater Lake, three more vents than were previously recognised. Upwellings appeared and disappeared in 10 min. Steam above the lake surface was controlled by both lake temperature and cloud conditions. Blue lakes were most common in summer and autumn, implying a relationship with ice or snow melt entering the lake. Grey lakes were observed in the month before 97% of eruptions, suggesting a correlation between a grey lake and eruption precursors.Crater Lake processes are illustrated by three regimes. Regime 1, a vigorously convecting grey lake associated with steam, eruptions, and more frequently the high end of recorded lake temperatures (within the range of 7 – 69 °C). Regime 2, a blue lake to occasionally green that typically occurs in summer and autumn when ice or snow melt is significant and is associated with generally the low end of lake temperatures and reduced volcanic fluid input. Regime 3, a blue-grey lake, the most observed lake colour in this study and effectively a balance between volcanic and seasonal processes. We suggest that an array of cameras would be useful additions to the current volcano monitoring network at Ruapehu.

Application of the Signaling Probe-Based DNA Microarray to Pathogenic Fungi Detection

Background and Purpose Microbiological testing is crucial for quality control in the production of foods and pharmaceuticals. Biochemical identification has been conventionally employed to detect and identify microorganisms. However, it requires time-consuming and labor-intensive processes as well as the skilled operators. To overcome the above problems, we have developed a novel DNA detection system; i.e. the signaling probe-based DNA microarray system towards pathogen detection1). The proposed system, which can eliminate labeling and washing steps, allows a simple and rapid detection of pathogenic bacteria genes. However, the proposed system has been only applied for bacterial gene detection based on 16S rDNA, but not for other pathogens including fungi. In this study, the signaling probe-based DNA microarray system was applied to the detection of three kinds of fungi, Aspergillus niger, Candida albicans, and Chaetomium globosum, common contaminants in food production. Materials and Methods The signaling probe-based DNA microarray is composed of a “fluorescence probe” (35-mer) labeled with Cy3 at the 5’-end and a “quencher probe” (35-mer) labeled with BHQ2 at the 3’-end. The quencher probe includes a detection sequence, which is complementary to the target fragments. Cy3-labeled and BHQ2-labeled probes were immobilized in the same spots. Partial hybridization between these probes makes the fluorophore and quencher molecules in close proximity, resulting in loss of fluorescence due to fluorescence resonance energy transfer (FRET). When the target fragments are introduced on the signaling probes, the fragments form duplexes with quenching probes by the detection sequence, and fluorescence signal is detectable. In this study, three signaling probe pairs were designed to detect the 18S rDNA genes, which is widely conserved in fungi. Target DNA fragments (245-bp to 247-bp; 18S rDNA) were prepared by asymmetric PCR using the genome from A. niger, C. albicans, and C. globosum. The PCR products were subjected to the signaling probe-based DNA microarray. Hybridization was carried out for 30 min at 60°C. The entire DNA microarray images were visualized in the aqueous solution using our original wide-field imaging system, consisting of a 532 nm laser source, microlens array, dichroic mirror, bandpass filter and CCD camera. The acquired images were processed using Array Pro image analysis software to analyze the fluorescence signals. Results & Discussion To confirm sufficient quenching of Cy3 on the fluorescence probe by BHQ2 on the quencher probe, the FRET-based quenching of the designed probe was investigated. In the absence of target DNA, the spots of the signaling probe exhibited no fluorescence, indicating that hybridization between the signaling probe pairs induced FRET. Next, the detection performance of the signaling probe-based DNA microarray was evaluated by measuring the fluorescence signals in the presence of target DNA. Clear fluorescence signals were observed in all the tested probes after the addition of target DNA. Furthermore, little fluorescence at the background levels were detected when using a control (PCR product of Escherichia coli 16S rDNA). These results suggest that the signaling probe-based DNA microarray can be useful for various pathogens including fungi. In our proposed system, removal of the target DNA solution and washing process were not required for detection because the wide-filed imaging system allowed us to visualize the fluorescence signals from the microarray immersed in the liquid media. The total assay was completed in 95 min. Therefore, the signaling probe-based DNA microarray allows a rapid and simple determination of a wide range of microorganisms.1) T. Taguchi, et al. Biosens. Bioelectron. 194, 113659 (2021)

A comprehensive research on light field imaging: Theory and application

AbstractComputational photography is a combination of novel optical designs and processing methods to capture high‐dimensional visual information. As an emerged promising technique, light field (LF) imaging measures the lighting, reflectance, focus, geometry and viewpoint in the free space, which has been widely explored for depth estimation, view synthesis, refocus, rendering, 3D displays, microscopy and other applications in computer vision in the past decades. In this paper, the authors present a comprehensive research survey on the LF imaging theory, technology and application. Firstly, the LF imaging process based on a MicroLens Array structure is derived, that is MLA‐LF. Subsequently, the innovations of LF imaging technology are presented in terms of the imaging prototype, consumer LF camera and LF displays in Virtual Reality (VR) and Augmented Reality (AR). Finally the applications and challenges of LF imaging integrating with deep learning models are analysed, which consist of depth estimation, saliency detection, semantic segmentation, de‐occlusion and defocus deblurring in recent years. It is believed that this paper will be a good reference for the future research on LF imaging technology in Artificial Intelligence era.

Investigation of ultrasmall sidewall-insulated GaN via with large aspect ratio for the strategy of vertically stacked full-color Micro-LEDs

Micro light-emitting diode (Micro-LED) is considered as an ideal candidate for near-eye, outdoor display, and light field photography applications. At present, the commercialization of full-color Micro-LED is limited by the mass transfer of red, green, and blue (R-B-G) sub-pixels. Hence, we proposed a full-color scheme of vertically stacked tricolor Micro-LED layers to avoid mass transfer, which owns over 1000 PPI (pixel per inch). In this solution, the sidewall-insulated via in the epilayer plays a critical role to achieve the electrical and mechanical integration of three monochromatic Micro-LEDs with Si-based complementary metal-oxide semiconductor (CMOS) driver. Therefore, the via processes of GaN-based epilayer were investigated systematically using available semiconductor processes in this article. The inductively coupled plasma (ICP) etching was employed to create the ultra-small micro-structure array using SiO2 thin film as hard mask. Sidewall-insulated vias were fabricated with different aperture sizes (9.3, 7.5, and 3.4 μm) and a depth of about 4-μm. The vias with large aspect ratio are completely satisfy the requirement of designed vertical interconnection. This study aims to provide valuable reference for the commercial progress of high-resolution and full-color Micro-LEDs.

Rapid acquisition and surface defects recognition based on panoramic image of small-section hydraulic tunnel

Small-section hydraulic tunnels are characterized by small spaces and various section forms, under complex environments, which makes it difficult to carry out an inspection by the mobile acquisition equipment. To resolve these problems, an arbitrarily adjustable camera module deployment method and the corresponding automatic image acquisition equipment with multi-area array cameras are proposed and developed. Such method enables the acquisition of full-length surface images of the hydraulic tunnels with different cross-section forms and diameters by a one-way travel, and the overlap rate and accuracy of the acquired image sets meet the requirements of three-dimensional reconstruction and panoramic image generation. In addition, to improve the speed and accuracy of traditional algorithms for tunnel surface defects detection, this paper proposes an improved YOLOv5s-DECA model. The algorithm introduces DenseNet to optimize the backbone feature extraction network and incorporates an efficient channel attention ECA module to make a better extraction of features of defects. The experimental results show that mAP, and F1-Score of YOLOv5-DECA are 73.4% and 74.6%, respectively, which are better than the common model in terms of accuracy and robustness. The proposed YOLOv5-DECA has great detection performance for targets with variable shapes and can solve the problem of classification imbalance in surface defects. Then, by combining YOLOv5-DECA with the direction search algorithm, a “point-ring-section” method is established to allow rapid identification of common surface defects by detecting them layer by layer with the bottom image of the stitched panorama as the seed. The presented method in this paper effectively solves the problem that a single image fails to show the overall distribution of the defects and their accurate positioning in a whole large tunnel section and the effective features of defects in an excessively large panoramic image size are difficult to be captured by the neural network. Field applications demonstrated that the presented method is adequate for high-precision and intelligent surface defect detection and positioning for different small-section hydraulic tunnels such as circular, arch-wall, and box-shaped hydraulic tunnels.

Ptychographic wavefront cameras.

We introduce a snapshot wavefront camera capable of reconstructing the complex field of an object with near quantum-limited accuracy. This system incorporates a diffraction grating at the entrance aperture and an array of cameras positioned to align with the grating orders, capturing the field with varying impulse responses. The camera effectively reconstructs both the amplitude and phase of the object. Key performance factors include the aperture size, the number of apertures, and the overlap ratio between adjacent apertures. The minimal imaging and computational performance could be obtained using a 3 × 3 camera array configuration.

Three-dimensional reconstruction of a light field based on phase restoration for highly reflective surfaces

This paper proposes, a novel, to our knowledge, phase-restoration-based light field method to achieve 3D reconstruction of highly reflective surfaces. First, a focused light field camera whose angular and spatial resolutions can be adjusted according to the needs has been designed and fabricated to capture 4D light field information. Then, according to the pixel offsets between different sub-aperture images, a phase restoration method based on multi-view complementary information is proposed to restore the missing absolute phase information caused by highlights. Finally, a cubic B-spline curve method is used to directly fit the relationship between absolute phase and coordinates to achieve 3D reconstruction of highly reflective surfaces. The experimental results demonstrate that the proposed method effectively utilizes the multi-view information from the light field to restore missing absolute phase data in the phase unwrapping, ensuring accurate 3D reconstruction of highly reflective surfaces. What is more, our method requires no additional hardware, camera angle calibration, or point cloud fusion, which significantly reduces both hardware complexity and computational demands.

Super-resolution imaging quality enhancement method for distributed array infrared camera

Abstract To address issues related to low resolution and high noise in infrared cameras, a distributed array infrared camera imaging system utilizing four cameras is proposed. The four cameras are arranged in an unconstrained array, and the combination algorithm of Projections onto Convex Sets (POCS) and Real-Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN) is applied to achieve high-quality super-resolution infrared imaging. The wavelet fusion algorithm is used to preprocess four low-resolution infrared images to reduce noise. Then, the POCS algorithm is used to reconstruct the preprocessed image. Finally, the Real-ESRGAN is employed for image reconstruction, resulting in an ultra-high-resolution infrared image. The results show that compared to single infrared camera imaging, the resolution of images reconstructed using the distributed infrared camera array is increased by 0.58 times, with the Modulation Transfer Function (MTF) increased by 1.2 times. Additionally, the entropy is increased by 18.87%, the standard deviation is increased by 13.51%, and the Naturalness Image Quality Evaluator (NIQE) is reduced by 18.87%. This demonstrates a significant enhancement in the super-resolution imaging quality of the distributed infrared camera array.