High Resolution Pixel Research Articles

Highly Enhanced Light Recycling in Quantum Dot Displays by Sidewall Reflectors

AbstractQuantum dot (QD) color conversion‐based displays have emerged as one of the most promising next‐generation devices due to their superior emission properties in terms of color expression. To date, however, existing QD color conversion layer (QD‐CCL) technologies have suffered from low luminance and power efficiency, mainly due to significant light absorption by the bank structure. Here, the color conversion efficiency of QD‐CCL has been significantly enhanced by fabricating a highly reflective metal layer on the side surface of the bank structure. Using a high‐aspect‐ratio silver reflector fabricated through a secondary sputtering lithographic technique involving argon ion bombardment, the fabricated QD‐CCL is combined with a blue organic light‐emitting diode (OLED) serving as a light source. As a result, light recycling from the reflector significantly enhances color conversion efficiency and luminance by up to 4.60‐fold and 4.29‐fold, respectively. Optical simulation reveals that higher pixel resolution provides greater reflection probabilities during extraction. This photomask‐free approach is not only simple but also highly compatible with existing semiconductor fabrication processes, making it a viable commercial alternative for all color‐converting structures that utilize light‐emitting materials with omnidirectional emission characteristics.

Ultrahigh-resolution, high-fidelity quantum dot pixels patterned by dielectric electrophoretic deposition

The high pixel resolution is emerging as one of the key parameters for the next-generation displays. Despite the development of various quantum dot (QD) patterning techniques, achieving ultrahigh-resolution (>10,000 pixels per inch (PPI)) and high-fidelity QD patterns is still a tough challenge that needs to be addressed urgently. Here, we propose a novel and effective approach of orthogonal electric field-induced template-assisted dielectric electrophoretic deposition to successfully achieve one of the highest pixel resolutions of 23090 (PPI) with a high fidelity of up to 99%. Meanwhile, the proposed strategy is compatible with the preparation of QD pixels based on perovskite CsPbBr3 and conventional CdSe QDs, exhibiting a wide applicability for QD pixel fabrication. Notably, we further demonstrate the great value of our approach to achieve efficiently electroluminescent QD pixels with a peak external quantum efficiency of 16.5%. Consequently, this work provides a general approach for realizing ultrahigh-resolution and high-fidelity patterns based on various QDs and a novel method for fabricating QD-patterned devices with high performance.

Retinal prosthesis edge detection (RPED) algorithm: Low-power and improved visual acuity strategy for artificial retinal implants.

This paper proposes a retinal prosthesis edge detection (RPED) algorithm that can achieve high visual acuity and low power. Retinal prostheses have been used to stimulate retinal tissue by injecting charge via an electrode array, thereby artificially restoring the vision of visually impaired patients. The retinal prosthetic chip, which generates biphasic current pulses, should be located in the foveal area measuring 5 mm × 5 mm. When a high-density stimulation pixel array is realized in a limited area, the distance between the stimulation pixels narrows, resulting in current dispersion and high-power dissipation related to heat generation. Various edge detection methods have been proposed over the past decade to reduce these deleterious effects and achieve high-resolution pixels. However, conventional methods have the disadvantages of high-power consumption and long data processing times because many pixels are activated to detect edges. In this study, we propose a novel RPED algorithm that has a higher visual acuity and less power consumption despite using fewer active pixels than existing techniques. To verify the performance of the devised RPED algorithm, the peak signal-to-noise ratio and structural similarity index map, which evaluates the quantitative numerical value of the image are employed and compared with the Sobel, Canny, and past edge detection algorithms in MATLAB. Finally, we demonstrate the effectiveness of the proposed RPED algorithm using a 1600-pixel retinal stimulation chip fabricated using a 0.35 μm complementary metal-oxide-semiconductor process.

7‐3: Analysis of a 1.2‐in. 4k2k LCOS Display Phase Modulator for Holographic Display Applications

This developed 1.2‐inch 4K2K LCOS with 76.0 lp/mm spatial resolution can still achieve high phase accuracy and obviously enlarge field‐of‐viewing (FOV) angle compared with previous 0.55‐inch 2K1K LCOS. The holographic image quality within phase‐only LCOS not only can be improved by using higher phase accuracy and compensation technologies, but also can realize the real 3D imaging into near‐eye displays to avoid the VAC phenomena. In addition to, the holographic 4K2K LCOS (26.6mm x 15.8mm) optical design can achieve lighter optical engine and reconstructed square (26.6mm x 26.6mm) image per eye, so that it has advantages of more feasible driving condition and cost competition. As high‐speed technologies of display electronics, liquid crystal, and optical algorithm were gradually mature, the above 1.2" phase‐only LCOS with both high pixel resolution and smaller pixel pitch will be another choice into the VR & MR headsets utilized video see‐through (VST) method. Meanwhile, the 1.2” 4K2K LCOS with enhanced FOV can be alternative scheme for AR glasses using optical see‐through (OST) method before the trade‐off between volume, brightness, and FOV angle are resolved.

Dewetting-assisted inkjet printing and optimized solvent mixture of Cd-free InP quantum dots for high-resolution pixel-pattern

Direct-printed quantum dot (QD) patterns utilizing inkjet method are a next-generation alternative to achieve high-resolution quantum dot light-emitting diodes (QLED). However, research on high-resolution pixel patterns is still limited due to issues such as thickness uniformity. In this study, we explored the feasibility of direct printing QLEDs to attain high-resolution pixel-patterned displays. Here, we successfully fabricated arrays of green QLED devices with a resolution of 200 pixels per inch, employing a precise 1 pL fine printer nozzle and optimizing ink formulation to achieve superior surface roughness and uniformity at the desired thickness. The inkjet-printed QD layers demonstrated a surface roughness of 1.9 nm and a flatness ratio of 0.2, comparable to those achieved through conventional spin-coating processes. This investigation offers the potential for extending the methodology to the fabrication of cost-effective, high-resolution full-color QLED displays.

Spatial-Logic-Aware Weakly Supervised Learning for Flood Mapping on Earth Imagery

Flood mapping on Earth imagery is crucial for disaster management, but its efficacy is hampered by the lack of high-quality training labels. Given high-resolution Earth imagery with coarse and noisy training labels, a base deep neural network model, and a spatial knowledge base with label constraints, our problem is to infer the true high-resolution labels while training neural network parameters. Traditional methods are largely based on specific physical properties and thus fall short of capturing the rich domain constraints expressed by symbolic logic. Neural-symbolic models can capture rich domain knowledge, but existing methods do not address the unique spatial challenges inherent in flood mapping on high-resolution imagery. To fill this gap, we propose a spatial-logic-aware weakly supervised learning framework. Our framework integrates symbolic spatial logic inference into probabilistic learning in a weakly supervised setting. To reduce the time costs of logic inference on vast high-resolution pixels, we propose a multi-resolution spatial reasoning algorithm to infer true labels while training neural network parameters. Evaluations of real-world flood datasets show that our model outperforms several baselines in prediction accuracy. The code is available at https://github.com/spatialdatasciencegroup/SLWSL.

Introducing the Video In Situ Snowfall Sensor (VISSS)

Abstract. The open-source Video In Situ Snowfall Sensor (VISSS) is introduced as a novel instrument for the characterization of particle shape and size in snowfall. The VISSS consists of two cameras with LED backlights and telecentric lenses that allow accurate sizing and combine a large observation volume with relatively high pixel resolution and a design that limits wind disturbance. VISSS data products include various particle properties such as maximum extent, cross-sectional area, perimeter, complexity, and sedimentation velocity. Initial analysis shows that the VISSS provides robust statistics based on up to 10 000 unique particle observations per minute. Comparison of the VISSS with the collocated PIP (Precipitation Imaging Package) and Parsivel instruments at Hyytiälä, Finland, shows excellent agreement with the Parsivel but reveals some differences for the PIP that are likely related to PIP data processing and limitations of the PIP with respect to observing smaller particles. The open-source nature of the VISSS hardware plans, data acquisition software, and data processing libraries invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.

DSAA-YOLO: UAV remote sensing small target recognition algorithm for YOLOV7 based on dense residual super-resolution and anchor frame adaptive regression strategy

The challenges posed by high pixel resolution and complex backgrounds in UAV remote sensing images have hindered the effective feature extraction and precise bounding box regression for small targets. In response to these challenges, numerous detection methods based on deep learning have emerged in recent years. Despite their advancements, these methods have not fully addressed the demand for accurate identification of small targets in remote sensing images. This paper introduces DSAA-YOLO, a novel algorithm designed for small target detection in UAV remote sensing images. Firstly, a new data augmentation strategy, termed Super Resolution Data Augment (SRDA), is proposed, which integrates the concept of image super-resolution to enrich the dataset while preserving data quality. Furthermore, a Dense Residual-based Super-Resolution module (DRSR) is introduced to enhance the resolution of small targets that have undergone quality degradation due to transformations. Subsequently, an Information Alignment Feature Enhancement Module (IAFE) is proposed to maximize the extraction of original features from the image. Finally, based on the improved Multi-Objective Grey Wolf Optimization (MOGWO), a novel dynamic anchor regression strategy termed Multi-Object Golf Dynamic Anchor (MGDA) is devised to generate more precise bounding boxes. The proposed DSAA-YOLO algorithm demonstrates significant improvements over current state-of-the-art methods in terms of widely recognized metrics including mAP, and AP50 on the VisDrone dataset.

A 3.02 pJ/Bit 3T-APS-Based In-Sensor Strong PUF Featuring Near-100% Hardware Reuse Ratio and High Resilience to Machine Learning Attacks

In this brief, we present an energy-efficient in-sensor strong physical unclonable function (PUF) based on the static entropy of 3-transistor active pixel sensor (3T-APS) structure that is widely-adopted in the CMOS image sensors (CIS). With ultra-small silicon area overhead of 0.25% dedicated to the added bias circuitries for column line and digital comparator for PUF bit generation, traditional 3T-APS-based CIS can be well-reused to construct the proposed strong PUF for authenticating the recorded images or videos. Taking advantage of the inherent high-resolution pixel array of CIS, the proposed strong PUF significantly extends the challenge-response-pair (CRP) space of the previous implementations. In addition, large CRP nonlinearity can be obtained by biasing the selected 3T-APS pixels at the subthreshold region, which further increases the complexity of the above extended CRP space and its resilience to various machine learning (ML) attacks. Moreover, the proposed strong PUF design is validated by the extensive measurement results of the prototype chips fabricated using a standard 65-nm CMOS process. Featuring a low energy consumption of 3.02 pJ/bit and a high area efficiency of 1.64×1033 bit/F2, prediction error of 49.1% 51.84% can be achieved under various ML attacks based on artificial neural network, logistic regression, support vector machine and covariance matrix adaptation evolution strategy, with the number of adopted CRPs for training up to 10 million.

Smart Parking Locks Based on Extended UNET-GWO-SVM Algorithm.

Due to the rapid increase in private car ownership in China, most cities face the problem of insufficient parking spaces, leading to frequent occurrences of parking space conflicts. There is a wide variety of parking locks available on the market. However, most of them lack advanced intelligence and cannot cater to the growing diverse needs of people. The present study attempts to devise a smart parking lock to tackle this issue. Specifically, the smart parking lock uses a Raspberry Pi as the core controller, senses the vehicle with an ultrasonic ranging module, and collects the license plate image with a camera. In addition, algorithms for license plate recognition based on traditional image-processing methods typically require a high pixel resolution, but their recognition accuracy is often low. Therefore, we propose a new algorithm called UNET-GWO-SVM to achieve higher accuracy in embedded systems. Moreover, we developed a WeChat mini program to control the smart parking lock. Field tests were conducted on campus to evaluate the performance of the parking locks. The test results show that the corresponding effective unlocking rate is 99.0% when the recognition error is less than two license plate characters. The average time consumption is controlled at about 2 s. It can meet real-time requirements.

An assisted multi-frame approach for super-resolution in hyperspectral images of rock samples

Imaging spectroscopy is decisive for accurately characterizing rock samples for geological and mineral applications, since slight differences in mineral composition can be easily recognized in spectral signatures. Although, instrument limitations prevent high spatial resolution pixels scanned along hundreds of spectral channels, which considerably reduces the data capabilities. Multi frame Super-Resolution (SR) techniques can overcome this issue by retrieving high-quality images using purely computational methods. However, these approaches still have important drawbacks because of inherent uncertainties when estimating the spatial variation between frames, which strongly penalize the spectral information, essential for lithology. In this paper we propose an assisted framework to provide fully controlled motion parameters of the imaging process, skipping the intricate registration task. Essentially, we use a stepping device to successively change the position of rock samples for each new frame acquisition. With accurate knowledge about motion parameters, we were able to overcome the uncertainties regarding spatial registration between frames. Therefore, a reasonable number of equations connecting the low-resolution frames and the super resolved image could be used to solve the optimization problem. Extensive experiments proved that the proposed assisted method achieved best performances (both qualitatively and quantitatively) compared with similar approaches. Moreover, detailed spectral analysis showed almost absolute consistency between the original low-resolution image and the super-resolved result, which is crucial to guarantee reliable mineral characterization.

P-625 Artificial intelligence-enabled analysis of endometrial CD138 positive plasma cells in infertility-associated conditions; Polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF)

Abstract Study question Can artificial intelligence (AI) algorithm identify CD138+ cells in the endometrial stroma, and is the expression altered in PCOS and RIF? Summary answer CD138+ cell detection by AI shows high accuracy. The cell count in controls was comparable to that in PCOS cases but differed in RIF patients. What is known already Immunohistochemical (IHC) staining for CD138+ plasma cells has been applied to detect endometrial inflammation; however, due to a lack of cost-effective and reliable tools, there is no golden standard for CD138+ analysis. PCOS is a common endocrine disorder characterized by irregular menstrual cycles and hyperandrogenism. The women are also affected by systemic low-grade inflammation, and the PCOS endometrium shows dysregulated immune profile. RIF is one of the main causes of a low IVF success rate, and chronic endometrial inflammation has been detected in women with RIF. To date, AI technology has been applied in the diagnostics of various clinical conditions. Study design, size, duration In this study, AI analysis was done for CD138+ cells in a total of 193 endometrial biopsy samples: 73 were obtained from healthy and fertile controls, 91 from women with PCOS (ovulatory samples; n = 78, anovulatory samples; n = 13, Rotterdam criteria), and 29 samples from RIF patients on day five after the initiation of P4 administration. Participants/materials, setting, methods The biopsy samples were collected by suction curette (Pipelle), and endometrial receptivity was assessed by gene expression profiling (beREADY test). The tissues were stained with CD138 antibody, and the AI algorithm, AITAH, was trained with 150 whole slide images (WSIs) to segment epithelium and stroma and segregate CD138- and CD138+ cells in the stroma. AITAH was validated by three external pathologists. The cell counts were compared according to cycle phases, ovulatory status, and endometrial receptivity. Main results and the role of chance The performance of AITHA was excellent, as the final training error for CD138+ cells was 3.23%, and the decisions for the cells between pathologists and AITAH were in complete agreement. The CD138+ cell percentages were higher in the proliferative phase (PE) endometrial tissue than in the ones obtained in the secretory phase (PCOS, p <0.001 or control, p <0.001), regardless of PCOS diagnosis. Similar CD138+ cell percentages were observed between PE endometrium and anovulatory endometrium in PCOS cases (p <0.001). The count of CD138+ plasma cells was negatively correlated with the endometrial thickness (PCOS, p =0.001 or control, p =0.009) and P4 (PCOS, p =0.001 or control, p =0.003), and positive correlations between the cell counts and gonadotropin levels were observed in ovulatory PCOS samples (FSH, p =0.001 and LH, p =0.031). More CD138+ cells were seen in RIF patients compared to the controls at the receptive stage (p = 0.046); however, there was no difference between RIF samples of three different receptivity statuses (p =0.426). Limitations, reasons for caution Further validation for AITAH using confirmed chronic endometritis cases as positive controls is required. And further analysis using control samples obtained during the hormone replacement treatment is required. The small number of anovulatory PCOS subjects may explain the absence of correlations between the CD138+ plasma cell counts and clinical characteristics. Wider implications of the findings AI-enabled analysis tools could improve histological examination of endometrial immune cells by analyzing high amounts of whole tissue slides within a short time with a high pixel resolution, thus overcoming many current impediments in manual microscopic evaluation. Trial registration number NA

A high-resolution pixel silicon Vertex Detector for open charm measurements with the NA61/SHINE spectrometer at the CERN SPS

The study of open charm meson production provides an efficient tool for the investigation of the properties of hot and dense matter formed in nucleus–nucleus collisions. The interpretation of the existing di-muon data from the CERN SPS suffers from a lack of knowledge on the mechanism and properties of the open charm particle production. Due to this, the heavy-ion programme of the NA61/SHINE experiment at the CERN SPS has been extended by precise measurements of charm hadrons with short lifetimes. A new Vertex Detector for measurements of the rare processes of open charm production in nucleus–nucleus collisions was designed to meet the challenges of track registration and high resolution in primary and secondary vertex reconstruction. A small-acceptance version of the vertex detector was installed in 2016 and tested with Pb + Pb collisions at 150A,text{ GeV }!/!c. It was also operating during the physics data taking on Xe + La and Pb + Pb collisions at 150A,text{ GeV }!/!c conducted in 2017 and 2018. This paper presents the detector design and construction, data calibration, event reconstruction, and analysis procedure.

Time-Dependent Systematic Biases in Inferring Ice Cloud Properties from Geostationary Satellite Observations

Geostationary satellite-based remote sensing is a powerful tool to observe and understand the spatiotemporal variation of cloud optical-microphysical properties and their climatologies. Solar reflectances measured from the Advanced Baseline Imager (ABI) instruments aboard Geostationary Operational Environmental Satellites 16 and 17 correspond to different spatial pixel resolutions, from 0.5 km in a visible band, up to 2 km in infrared bands. For multi-band retrievals of cloud properties, reflectances with finer spatial resolution need to be resampled (averaged or sub-sampled) to match the coarsest resolution. Averaging all small pixels within a larger pixel footprint is more accurate but computationally demanding when the data volume is large. Thus, NOAA operational cloud products incorporate sub-sampling (selecting one high-resolution pixel) to resample the reflectance data, which could cause potential retrieval biases. In this study, we examine various error sources of retrieval biases of cloud optical thickness (COT) and cloud effective radius (CER) caused by sub-sampling, including the solar zenith angle, viewing zenith angle, pixel resolutions, and cloud types. CER retrievals from ice clouds based on sub-sampling have larger biases and uncertainties than COT retrievals. The relative error compared to pixel averaging is positive for clouds that have small COT or CER, and negative for clouds that have large COT or CER. The relative error of COT decreases as the pixel resolution becomes coarser. The COT retrieval biases are attributed mainly to cirrus and cirrostratus clouds, while the largest biases of CER retrievals are associated with cirrus clouds.

Super-resolution computed tomography imaging spectrometry

Computed tomography imaging spectrometry (CTIS) is a snapshot spectral imaging technique that relies on a limited number of projections of the target data cube (2D spatial and 1D spectral), which can be reconstructed via a delicate tomographic reconstruction algorithm. However, the restricted angle difference between the projections and the space division multiplexing of the projections make the reconstruction suffer from severe artifacts as well as a low spatial resolution. In this paper, we demonstrate super-resolution computed tomography imaging spectrometry (SRCTIS) by assimilating the information obtained by a conventional CTIS system and a regular RGB camera, which has a higher pixel resolution. To improve the reconstruction accuracy of CTIS, the unique information provided by the zero-order diffraction of the target scene is used as a guidance image for filtering to better preserve the edges and reduce artifacts. The recovered multispectral image is then mapped onto the RGB image according to camera calibration. Finally, based on the spectral and the spatial continuities of the target scene, the multispectral information obtained from CTIS is propagated to each pixel of the RGB image to enhance its spectral resolution, resulting in SRCTIS. Both stimulative studies and proof-of-concept experiments were then conducted, and the results quantified by key metrics, such as structural similarity index measurement and spectral angle mapping have suggested that the developed method cannot only suppress the reconstruction artifacts, but also simultaneously achieve high spatial and spectral resolutions.

Cascade-refine model for cephalometric landmark detection in high-resolution orthodontic images

Landmark detection has been well developed by deep learning methods, and the cascade-connected neural network (CCNN) stands out as a widely used deep learning landmark detection method. CCNNs consist of several stacked network backbones, where the predictions of the previous network backbone are used as the input of the following one. Due to GPU memory bottlenecks, CCNNs have two limitations. First, the network backbones of CCNNs have limited numbers and depths; thus, the learning ability of CCNNs is limited. Second, CCNNs are usually trained in low-resolution images. However, the neighboring pixels in high-resolution images are usually vital for landmark detection, especially for cephalometric landmark detection. This paper interprets CCNNs as the discrete approximation of ordinary differential equations. Relying on this explanation, we further propose a novel model, called the cascade-refine model, which takes advantage of CCNNs and makes it possible to overcome the limitations of number and depth by sharing parameters among stacked network backbones. Moreover, the proposed model obeys the rule of coarse-to-fine architectures, where a global module is used to generate the coarse landmark locations, and a local module is adopted to tune the pixel error of latent landmark locations in the region of interest. The proposed model is trained in an end-to-end manner. Thus, the neighboring detailed high-resolution pixels are directly exploited for cephalometric landmark detection. The proposed cascade-refine model outperforms state-of-the-art methods on the public Automatic Cephalometric X-ray Landmark Detection Challenge 2015 dataset. We also built two private cephalometric X-ray datasets, and experimental results on both datasets demonstrate the good performance of the proposed model.

Methodology for fine-scale seamless elevation model construction using unoccupied aerial and surface vehicles

Seamless bathymetric and topographic digital elevation models (SBT-DEM) of coastal zones are important for modeling tsunami propagation and coastal inundation, monitoring coastal erosion and deposition, and modeling hydrodynamics and sediment transport. Fine-scale SBT-DEM construction has been elusive due to the difficulty of accurately measuring shallow water depths with conventional techniques. We attempt to perform coastal bathymetric and topographic surveys using an SBES-equipped unoccupied surface vehicle (USV) and a LiDAR-equipped unoccupied aerial vehicle (UAV), and construct a fine-scale SBT-DEM using GIS (Geographic Information System) techniques. The results show that the proposed spatiotemporal divisional measurement method can seamlessly collect coastal topographic and bathymetric data using tidal range. The improved Blend mosaic algorithm not only seamlessly and smoothly stitches the land and seabed DEMs, but also minimizes the loss of elevation accuracy. The fine-scale SBT-DEM constructed in this paper has the advantages of high resolution (Pixel size = 1 m) and high accuracy (RMSE = 6.2 cm). Fine-scale SBT-DEM can provide integrated topographic and bathymetric data for coastal dynamic monitoring and environmental management.

Absorption of pressurized methane in normal and supercooled p-xylene revealed via high-resolution neutron imaging

Supercooling of liquids leads to peculiarities which are scarcely studied under high-pressure conditions. Here, we report the surface tension, solubility, diffusivity, and partial molar volume for normal and supercooled liquid solutions of methane with p-xylene. Liquid bodies of perdeuterated p-xylene (p-C8D10), and, for comparison, o-xylene (o-C8D10), were exposed to pressurized methane (CH4, up to 101 bar) at temperatures ranging 7.0–30.0 °C and observed at high spatial resolution (pixel size 20.3 μm) using a non-tactile neutron imaging method. Supercooling led to the increase of diffusivity and partial molar volume of methane. Solubility and surface tension were insensitive to supercooling, the latter substantially depended on methane pressure. Overall, neutron imaging enabled to reveal and quantify multiple phenomena occurring in supercooled liquid p-xylene solutions of methane under pressures relevant to the freeze-out in the production of liquefied natural gas.

Spatiotemporal image quality of virtual reality head mounted displays

Virtual reality (VR) head mounted displays (HMDs) require both high spatial resolution and fast temporal response. However, methods to quantify the VR image quality in the spatiotemporal domain when motion exists are not yet standardized. In this study, we characterize the spatiotemporal capabilities of three VR devices: the HTC VIVE, VIVE Pro, and VIVE Pro 2 during smooth pursuit. A spatiotemporal model for VR HMDs is presented using measured spatial and temporal characteristics. Among the three evaluated headsets, the VIVE Pro 2 improves the display temporal performance using a fast 120 Hz refresh rate and pulsed emission with a small duty cycle of 5%. In combination with a high pixel resolution beyond 2 k times 2 k per eye, the VIVE Pro 2 achieves an improved spatiotemporal performance compared to the VIVE and VIVE Pro in the high spatial frequency range above 8 cycles per degree during smooth pursuit. The result demonstrates that reducing the display emission duty cycle to less than 20% is beneficial to mitigate motion blur in VR HMDs. Frame rate reduction (e.g., to below 60 Hz) of the input signal compared to the display refresh rate of 120 Hz yields replicated shadow images that can affect the image quality under motion. This work supports the regulatory science research efforts in development of testing methods to characterize the spatiotemporal performance of VR devices for medical use.

Information fusion approach for downscaling coarse resolution scatterometer data

ABSTRACT The applications of scatterometer data (σ°) are limited due to their coarser resolution (25–50 km). Some image reconstruction techniques are available to generate high-resolution products, but they require various sensor parameters and multiset observation, making them complex to use. Therefore, this paper proposes an information fusion approach to disaggregate the coarse resolution σ° product. The coarse resolution backscattering signal includes the contribution from more than one land cover class, such as short vegetation, soil, urban and tall vegetation, the information of which can be obtained from normalised difference vegetation index (NDVI), vegetation temperature condition index (VTCI), and fraction cover of urban and forests, respectively. Disaggregating this coarse resolution pixel, an optimum weight information is required that provides the distribution of each class. Since the distribution of land cover classes is not homogeneous for every pixel, a variance-based fusion approach has been used to obtain the optimum weight factors to fuse NDVI, VTCI, and fraction cover. These weight factors are used to disaggregate every coarse-resolution pixel into high-resolution pixels. The developed model is applied to Sentinel-1 and Scatsat-1 level-3 products, and the obtained results are quite satisfactory.