High Depth Resolution Research Articles

Investigating the depolarization property of skin tissue by degree of polarization uniformity contrast using polarization-sensitive optical coherence tomography.

The depolarization property of skin has been found to be important for skin cancer detection. Previous techniques based on light polarization lack the capability of depth differentiation. Polarization-sensitive optical coherence tomography (PS-OCT) has the advantage of both depth-resolved 3D imaging and high sensitivity to polarization. In this study, we investigate the depolarization property of skin tissue using PS-OCT, especially with the degree of polarization uniformity (DOPU) contrast. Well designed skin phantoms with various surface roughness levels and optical properties mimicking skin are imaged by PS-OCT and the DOPU values are quantified. The result shows a correlation between DOPU and surface roughness, where a higher roughness corresponds to a lower DOPU value. An index matching experiment with a water layer confirms the impact of surface condition on light depolarization. Refraction of backscattered photons on the surface boundary is attributed to the broadening of backscattering angle and thus depolarization. To the best of our knowledge, this is the first time the impact of surface roughness on DOPU is reported and its mechanism explained. Furthermore, through preliminary in vivo skin imaging, the capability of DOPU in detecting depolarization in skin is demonstrated. By utilizing the 3D imaging from PS-OCT, DOPU can offer a high-resolution depth differentiation and quantification of depolarization in skin tissue.

Unpiloted Aerial Vehicle Retrieval of Snow Depth Over Freshwater Lake Ice Using Structure From Motion

The presence and thickness of snow overlying lake ice affects both the timing of melt and ice-free conditions, can contribute to overall ice thickness through its insulative capacity, and fosters the development of variable ice types. The use of UAVs to retrieve snow depths with high spatial resolution is necessary for the next generation of ultra-fine hydrological models, as the direct contribution of water from snow on lake ice is unknown. Such information is critical to the understanding of the physical processes of snow redistribution and capture in catchments on small lakes in the Arctic, which has been historically estimated from its relationship to terrestrial snowpack properties. In this study, we use a quad-copter UAV and SfM principles to retrieve and map snow depth at the winter maximum at high resolution over a the freshwater West Twin Lake on the Arctic Coastal Plain of northern Alaska. The accuracy of the snow depth retrievals is assessed using in-situ observations (n = 1,044), applying corrections to account for the freeboard of floating ice. The average snow depth from in-situ observations was used calculate a correction factor based on the freeboard of the ice to retrieve snow depth from UAV acquisitions (RMSE = 0.06 and 0.07 m for two transects on the lake. The retrieved snow depth map exhibits drift structures that have height deviations with a root mean square (RMS) of 0.08 m (correlation length = 13.8 m) for a transect on the west side of the lake, and an RMS of 0.07 m (correlation length = 18.7 m) on the east. Snow drifts present on the lake also correspond to previous investigations regarding the variability of snow on lakes, with a periodicity (separation) of 20 and 16 m for the west and east side of the lake, respectively. This study represents the first retrieval of snow depth on a frozen lake surface from a UAV using photogrammetry, and promotes the potential for high-resolution snow depth retrieval on small ponds and lakes that comprise a significant portion of landcover in Arctic environments.

Continuous flow analysis methods for sodium, magnesium and calcium detection in the Skytrain ice core

AbstractDissolved and particulate sodium, magnesium and calcium are analyzed in ice cores to determine past changes in sea ice extent, terrestrial dust variability and atmospheric aerosol transport efficiency. They are also used to date ice cores if annual layers are visible. Multiple methods have been developed to analyze these important compounds in ice cores. Continuous flow analysis (CFA) is implemented with instruments that sample the meltstream continuously. In this study, CFA with ICP-MS (inductively coupled-plasma mass spectrometry) and fast ion chromatography (FIC) methods are compared for analysis of sodium and magnesium. ICP-MS, FIC and fluorescence methods are compared for analysis of calcium. Respective analysis of a 10 m section of the Antarctic WACSWAIN Skytrain Ice Rise ice core shows that all of the methods result in similar levels of the compounds. The ICP-MS method is the most suitable for analysis of the Skytrain ice core due to its superior precision (relative standard deviation: 1.6% for Na, 1.3% for Mg and 1.2% for Ca) and sampling frequency compared to the FIC method. The fluorescence detection method may be preferred for calcium analysis due to its higher depth resolution (1.4 cm) relative to the ICP-MS and FIC methods (~4 cm).

3D reconstruction with auto-selected keyframes based on depth completion correction and pose fusion

Dense 3D reconstruction is required for robots to safely navigate or perform advanced tasks. The accurate depth information of the image and its pose are the basis of 3D reconstruction. The resolution of depth maps obtained by LIDAR and RGB-D cameras is limited, and traditional pose calculation methods are not accurate enough. In addition, if each image is used for dense 3D reconstruction, the dense point clouds will increase the amount of calculation. To address these issues, we propose a 3D reconstruction system. Specifically, we propose a depth network of contour and gradient attention, which is used to complete and correct depth maps to obtain high-resolution and high-quality depth maps. Then, we propose a method of fusion of traditional algorithms and deep learning for pose estimation to obtain accurate localization results. Finally, we adopt the method of autonomous selection of keyframes to reduce the number of keyframes, the surfel-based geometric reconstruction is performed to reconstruct the dense 3D environment. On the TUM RGB-D, ICL-NIUM, and KITTI datasets, our method significantly improves the quality of the depth maps, the localization results, and the effect of 3D reconstruction. At the same time, we have also accelerated the speed of 3D reconstruction.

Ex-vivo confocal Raman microspectroscopy of porcine skin with 633/785-NM laser excitation and optical clearing with glycerol/water/DMSO solution

Confocal Raman microspectroscopy (CRM) with 633- and 785-nm excitation wavelengths combined with optical clearing (OC) technique was used for ex-vivo study of porcine skin in the Raman fingerprint region. The optical clearing has been performed on the skin samples by applying a mixture of glycerol and distilled water and a mixture of glycerol, distilled water and chemical penetration enhancer dimethyl sulfoxide (DMSO) during 30[Formula: see text]min and 60[Formula: see text]min of treatment. It was shown that the combined use of the optical clearing technique and CRM at 633[Formula: see text]nm allowed one to preserve the high probing depth, signal-to-noise ratio and spectral resolution simultaneously. Comparing the effect of different optical clearing agents on porcine skin showed that an optical clearing agent containing chemical penetration enhancer provides higher optical clearing efficiency. Also, an increase in treatment time allows to improve the optical clearing efficiency of both optical clearing agents. As a result of optical clearing, the detection of the amide-III spectral region indicating well-distinguishable structural differences between the type-I and type-IV collagens has been improved.

Robust real-time 3D imaging of moving scenes through atmospheric obscurant using single-photon LiDAR

Recently, time-of-flight LiDAR using the single-photon detection approach has emerged as a potential solution for three-dimensional imaging in challenging measurement scenarios, such as over distances of many kilometres. The high sensitivity and picosecond timing resolution afforded by single-photon detection offers high-resolution depth profiling of remote, complex scenes while maintaining low power optical illumination. These properties are ideal for imaging in highly scattering environments such as through atmospheric obscurants, for example fog and smoke. In this paper we present the reconstruction of depth profiles of moving objects through high levels of obscurant equivalent to five attenuation lengths between transceiver and target at stand-off distances up to 150 m. We used a robust statistically based processing algorithm designed for the real time reconstruction of single-photon data obtained in the presence of atmospheric obscurant, including providing uncertainty estimates in the depth reconstruction. This demonstration of real-time 3D reconstruction of moving scenes points a way forward for high-resolution imaging from mobile platforms in degraded visual environments.

Quantification of High Resolution Pulsed RF GDOES Depth Profiles for Mo/B4C/Si Nano-Multilayers

Pulsed-radio frequency glow discharge optical emission spectrometry (Pulsed-RF-GDOES) has exhibited great potential for high resolution (HR) depth profiling. In this paper, the measured GDOES depth profile of 60 × Mo (3 nm)/B4C (0.3 nm)/Si (3.7 nm) was quantified by employing the newly extended Mixing-Roughness-Information depth (MRI) model. We evaluated the influences of the thickness and sputtering rate on the depth profile of very thin layers. We demonstrated that a method using the full width at half maximum (FWHM) value of the measured time-concentration profile for determining the sputtering rate and the corresponding thickness was not reliable if preferential sputtering took place upon depth profiling.

Three-Dimensional Laser Imaging with a Variable Scanning Spot and Scanning Trajectory

Traditional lidar scans the target with a fixed-size scanning spot and scanning trajectory. Therefore, it can only obtain the depth image with the same pixels as the number of scanning points. In order to obtain a high-resolution depth image with a few scanning points, we propose a scanning and depth image reconstruction method with a variable scanning spot and scanning trajectory. Based on the range information and the proportion of the area of each target (PAET) contained in the multi echoes, the region with multi echoes (RME) is selected and a new scanning trajectory and smaller scanning spot are used to obtain a finer depth image. According to the range and PAET obtained by scanning, the RME is segmented and filled to realize the super-resolution reconstruction of the depth image. By using this method, the experiments of two overlapped plates in space are carried out. By scanning the target with only forty-three points, the super-resolution depth image of the target with 160 × 160 pixels is obtained. Compared with the real depth image of the target, the accuracy of area representation (AOAR) and structural similarity (SSIM) of the reconstructed depth image is 99.89% and 98.94%, respectively. The method proposed in this paper can effectively reduce the number of scanning points and improve the scanning efficiency of the three-dimensional laser imaging system.

Isotopic fingerprinting of biogeochemical processes and iron sources in the iron-limited surface Southern Ocean

Iron (Fe) is an essential micronutrient that limits primary productivity throughout the surface of the Southern Ocean. Here, we present the first high-resolution depth profiles for dissolved Fe and Fe isotope ratios (δ56Fe) from all major zones of the Southern Ocean, collected during the Antarctic Circumnavigation Expedition in austral summer 2017. Open-ocean surface waters are characterized by remarkably high δ56Fe values (up to +1.6‰) and very low Fe concentrations (<0.05 nmolkg−1). We attribute the elevated δ56Fe values above the ferricline to the effect of continuous shallow cycling processes (uptake, recycling, and binding of Fe to organic ligands), with only a very limited resupply of Fe from below. Below the ferricline, δ56Fe values approach ∼0‰ and remain constant down to our deepest samples at 1000 m, with no obvious isotope signal from regeneration. This overall pattern in δ56Fe is modified near islands, continental shelves and hydrothermal vents, where distinct δ56Fe signatures are associated with different Fe sources. Near the volcanic Balleny Islands, elevated surface Fe concentrations associated with low δ56Fe are indicative of reductive release of isotopically light Fe from sediments. Elevated δ56Fe values at depth near the Balleny seamount chain and near the East Scotia Arc may reflect distal hydrothermal influences, caused by fractionation associated with precipitation or the loss of specific phases of Fe during long-range transport. Sedimentary sources of isotopically light Fe on the Antarctic Peninsula are important for shelf waters. Long-distance transport of this sediment-derived Fe and its influence on surface waters are strongly dependent on the regional circulation, and may ultimately be the source of light Fe previously observed within Antarctic Intermediate Water in the Atlantic sector of the Southern Ocean.

A Case Study of Thermal Evolution in the Vicinity of Geothermal Probes Following a Distributed TRT Method

To meet the stated climate change targets and to ensure the capability of meeting the current and future energy demands, there is an urgent need to develop renewable energy sources, such as geothermal systems. If geothermal systems are to be cost-efficient and are to enjoy public confidence, it is essential that they are designed and installed in accordance with the prevailing site-specific conditions. A thorough understanding of the thermal behaviour of the surrounding ground is, therefore, critical. In this work, we investigated temperature and its evolution in the vicinity of a shallow geothermal helix-shaped borehole heat exchanger (BHE). To measure the temperature close to the actual geothermal system, an additional U-tube probe was installed at the edge of the same borehole. A thermal load was then applied to the BHE, and the temperature was detected in the nearby U-tube. The temperature measurements were made with a GEOSniff monitoring device. To understand these localised temperature measurements in the context of the Valencia test site, ERT measurements were also performed. The GEOSniff device permits measurements to be made with very high depth resolution, which allows the thermal properties of the surrounding ground to be derived precisely, thus, enabling the identification of the different textural domains.

DAEANet: Dual auto-encoder attention network for depth map super-resolution

Recently, depth map super-resolution (DSR) has obtained remarkable performance with the development of convolutional neural networks (CNNs). High-resolution (HR) depth map can be inferred from a low-resolution (LR) one with the guidance of its corresponding HR intensity image. However, most of the existing CNNs-based methods unilaterally transfer structures information of guidance image to the input depth map, which ignores the corresponding relations between the depth map and the intensity map. In this paper, we propose a novel dual auto-encoder attention network (DAEANet) for DSR. The proposed DAEANet includes two auto-encoder networks, where guidance auto-encoder network (GAENet) and target auto-encoder network (TAENet) aim to extract feature information from intensity image and depth map. Specifically, all auto-encoder networks are similar and trained simultaneously to ensure structural consistency. Furthermore, to preserve the structure information in the process of training, the attention mechanism is employed to our DAEANet. Extensive experiments on several popular benchmarks show that the proposed DAEANet outperforms existing state-of-the-art algorithms.

Adaptive depth estimation for pyramid multi-view stereo

In this paper, we propose a Multi-View Stereo (MVS) network which can perform efficient high-resolution depth estimation with low memory consumption. Classical learning-based MVS approaches typically construct 3D cost volumes to regress depth information, making the output resolution rather limited as the memory consumption grows cubically with the input resolution. Although recent approaches have made significant progress in scalability by introducing the coarse-to-fine fashion or sequential cost map regularization, the memory consumption still grows quadratically with input resolution and is not friendly for commodity GPU. Observing that the surfaces of most objects in real world are locally smooth, we assume that most of the depth hypotheses upsampled from a well-estimated depth map are accurate. Based on the assumption, we propose a pyramid MVS network based on the adaptive depth estimation, which gradually refines and upsamples the depth map to the desired resolution. Instead of estimating depth hypotheses for all pixels in the depth map, our method only performs prediction at adaptively selected locations, alleviating excessive computation on well-estimated positions. To estimate depth hypotheses for sparse selected locations, we propose the lightweight pixelwise depth estimation network, which can estimate depth value for each selected location independently. Experiments demonstrate that our method can generate results comparable with the state-of-the-art learning-based methods while reconstructing more geometric details and consuming less GPU memory.

Determination of parameters for self-stratification in bimodal colloidal coatings using Raman depth profiling

In waterborne mixtures of colloidal particles with differing sizes, the spontaneous stratification of one species of particle in a coating – driven by diffusiophoresis - offers the possibility to tailor the surface properties. However, despite strong research interest in stratification in recent years, the acceptable range of experimental parameters has not been fully explored, and the extent of stratification that is achievable has not yet been quantified. Here, we study the stratification of bimodal mixtures of waterborne polyurethane particles mixed with larger acrylic particles. We use ultra-low angle microtoming to prepare cross-sections of coating samples and analyse compositions quantitatively with Raman mapping. We use this method to obtain high-resolution depth profiles of the polyurethane phase in the coating with spacing between measurements corresponding to a few tens of nm. We experimentally test a model of diffusiophoresis and observe stratification when the processing parameters (evaporation rates, film thickness, and volume fraction of small particles) fall within the required range. Samples that exhibit stratification have top layer thicknesses on the order of tens of μm, which is a significant depth for exploitation in coatings aiming to modify surface properties. To guide the design of coatings in applications, we draw on the model to define the range of parameters in which self-stratification is expected. Our results provide a fundamental understanding that will enable the fabrication of tailored coatings in which the properties of the surface differ from the bulk material.

Neuromodulation Effect of Very Low Intensity Transcranial Ultrasound Stimulation on Multiple Nuclei in Rat Brain.

ObjectiveLow-intensity transcranial ultrasound stimulation (TUS) is a non-invasive neuromodulation technique with high spatial resolution and feasible penetration depth. To date, the mechanisms of TUS modulated neural oscillations are not fully understood. This study designed a very low acoustic intensity (AI) TUS system that produces considerably reduced AI Ultrasound pulses (ISPTA < 0.5 W/cm2) when compared to previous methods used to measure regional neural oscillation patterns under different TUS parameters.MethodsWe recorded the local field potential (LFP) of five brain nuclei under TUS with three groups of simulating parameters. Spectrum estimation, time-frequency analysis (TFA), and relative power analysis methods have been applied to investigate neural oscillation patterns under different stimulation parameters.ResultsUnder PRF, 500 Hz and 1 kHz TUS, high-amplitude LFP activity with the auto-rhythmic pattern appeared in selected nuclei when ISPTA exceeded 12 mW/cm2. With TFA, high-frequency energy (slow gamma and high gamma) was significantly increased during the auto-rhythmic patterns. We observed an initial plateau in nuclei response when ISPTA reached 16.4 mW/cm2 for RPF 500 Hz and 20.8 mW/cm2 for RPF 1 kHz. The number of responding nuclei started decreasing while ISPTA continued increasing. Under 1.5 kHz TUS, no auto-rhythmic patterns have been observed, but slow frequency power was increased during TUS. TUS inhibited most of the frequency band and generated obvious slow waves (theta and delta band) when stimulated at RPF = 1.5 kHz, ISPTA = 8.8 mW/cm2.ConclusionThese results demonstrate that very low intensity Transcranial Ultrasound Stimulation (VLTUS) exerts significant neuromodulator effects under specific parameters in rat models and may be a valid tool to study neuronal physiology.

Attention aware cost volume pyramid based multi-view stereo network for 3D reconstruction

We present an efficient multi-view stereo (MVS) network for 3D reconstruction from multi-view images. While previous learning based reconstruction approaches performed quite well, most of them estimate depth maps at a fixed resolution using plane sweep volumes with a fixed depth hypothesis at each plane, which requires densely sampled planes for desired accuracy and therefore is difficult to achieve high resolution depth maps. In this paper we introduce a coarse-to-fine depth inference strategy to achieve high resolution depth. This strategy first estimates the depth map at coarsest level, and the depth maps at finer levels are considered as the upsampled depth map from previous level with pixel-wise depth residual. Thus, we narrow the depth searching range with the priori information from previous level and construct new cost volumes from the pixel-wise depth residual to perform depth map refinement. Then the final depth map can be achieved iteratively since all the parameters are shared among different levels. At each level, the self-attention layer is introduced to the feature extraction block for capturing the important information in depth inference task, and the cost volume is generated using similarity measurement instead of the variance based methods used in previous work. Experiments were conducted on three diverse datasets including the DTU benchmark dataset, BlendedMVS dataset and the Tanks and Temples dataset. The results demonstrated that our proposed approach could outperform most state-of-the-arts (SOTA) methods.22The codebase of this project is at https://github.com/ArthasMil/AACVP-MVSNet.

4D surface shape measurement system with high spectral resolution and great depth accuracy.

A 4D surface shape measurement system that combines spectral detection and 3D surface morphology measurements is proposed, which can realize high spectral resolution and great depth accuracy (HSDA system). A starring hyperspectral imager system based on a grating generates precise spectral data, while a structured light stereovision system reconstructs target morphology as a 3D point cloud. The systems are coupled using a double light path module, which realize point-to-point correspondence of the systems' image planes. The spectral and 3D coordinate data are fused and transformed into a 4D data set. The HSDA system has excellent performance with a spectral resolution of 3 nm and depth accuracy of 27.5 μm. A range of 4D imaging experiments are presented to demonstrate the capabilities and versatility of the HSDA system, which show that it can be used in broad range of application areas, such as fluorescence detection, face anti-spoofing, physical health state assessment and green plant growth condition monitoring.

An encoder-decoder deep surrogate for reverse time migration in seismic imaging under uncertainty

Seismic imaging faces challenges due to the presence of several uncertainty sources. Uncertainties exist in data measurements, source positioning, and subsurface geophysical properties. Reverse time migration (RTM) is a high-resolution depth migration approach useful for extracting information such as reservoir localization and boundaries. RTM, however, is time-consuming and data-intensive as it requires computing twice the wave equation to generate and store an imaging condition. RTM, when embedded in an uncertainty quantification algorithm (like the Monte Carlo method), shows a many-fold increase in its computational complexity due to the high input-output dimensionality. In this work, we propose an encoder-decoder deep learning surrogate model for RTM under uncertainty. Inputs are an ensemble of velocity fields, expressing the uncertainty, and outputs the seismic images. We show by numerical experimentation that the surrogate model can reproduce the seismic images accurately, and, more importantly, the uncertainty propagation from the input velocity fields to the image ensemble.

Real-time, High-resolution Depth Upsampling on Embedded Accelerators

High-resolution, low-latency apps in computer vision are ubiquitous in today’s world of mixed-reality devices. These innovations provide a platform that can leverage the improving technology of depth sensors and embedded accelerators to enable higher-resolution, lower-latency processing for 3D scenes using depth-upsampling algorithms. This research demonstrates that filter-based upsampling algorithms are feasible for mixed-reality apps using low-power hardware accelerators. The authors parallelized and evaluated a depth-upsampling algorithm on two different devices: a reconfigurable-logic FPGA embedded within a low-power SoC; and a fixed-logic embedded graphics processing unit. We demonstrate that both accelerators can meet the real-time requirements of 11 ms latency for mixed-reality apps. 1

Research on computer vision enhancement in intelligent robot based on machine learning and deep learning

The stable operation of intelligent robots requires the effective support of machine vision technology. In order to improve the effect of robot machine vision recognition, based on deep learning, this paper, under the guidance of machine learning ideas, proposes a target detection framework that combines target recognition and target tracking based on the efficiency advantages of the KCF visual tracking algorithm. Moreover, this paper designs a vision system based on a high-resolution color camera and TOF depth camera. In addition, by modeling the coordinate conversion relationship of the same object in the camera coordinate system of two cameras, the projection relationship of the depth map collected by the TOF camera to the pixel coordinate system of the high-resolution color camera is determined. In addition, this paper designs experiments to verify the performance of the model. The research results show that the method proposed in this paper has a certain effect.

High-resolution CubeSat imagery and machine learning for detailed snow-covered area

Snow cover affects a diverse array of physical, ecological, and societal systems. As such, the development of optical remote sensing techniques to measure snow-covered area (SCA) has enabled progress in a wide variety of research domains. However, in many cases, the spatial and temporal resolutions of currently available remotely sensed SCA products are insufficient to capture SCA evolution at spatial and temporal resolutions relevant to the study of fine-scale spatially heterogeneous phenomena. We developed a convolutional neural network-based method to identify snow covered area using the ~3 m, 4-band PlanetScope optical satellite image dataset with ~daily, near-global coverage. By comparing our model performance to snow extent derived from high-resolution airborne lidar differential depth measurements and satellite platforms in two North American sites (Sierra Nevada, CA, USA and Rocky Mountains, CO, USA), we show that these emerging image archives have great potential to accurately observe snow-covered area at high spatial and temporal resolutions despite limited radiometric bandwidth and band placement. We achieve average snow classification F-Scores of 0.73 in our training basin and 0.67 in a climatically-distinct out-of-sample basin, suggesting opportunities for model transferability. We also evaluate the performance of these data in forested regions, suggesting avenues for further research. The unparalleled spatial and temporal coverage of CubeSat imagery offers an excellent opportunity for satellite remote sensing of snow, with real implications for ecological and water resource applications.