High-resolution Output Research Articles

Luminescence Properties of Bi3+-Doped Gd2O3 Phosphor under UV and X-ray Excitation.

X-ray excited luminescence in scintillators plays a crucial role in applications ranging from medical diagnostics to industrial quality inspection and scientific research. This study explores the potential of Bi3+-doped Gd2O3 phosphors as next-generation scintillators, leveraging their broad-spectrum emission characteristics to achieve higher light yields. We synthesized Gd2O3:Bi3+ phosphors in two crystalline phases─cubic and monoclinic─using a high-temperature solid-phase method. These phosphors demonstrate varied applications, from backlighting displays to X-ray imaging of opaque and biological samples. Under UV light excitation, each phase exhibits distinct luminescence properties due to different Bi3+ occupancy environments. The monoclinic phase, excited at λex = 370 nm, showed stronger luminescence and a broader emission peak (fwhm = 70 nm) with a higher photoluminescence quantum yield (67.94%), compared to the cubic phase, which under optimal conditions (λex = 377 nm) exhibited narrower peaks (fwhm = 40 nm) and a lower yield (44.41%). Notably, under X-ray excitation, the cubic phase outperformed with a higher light yield (48,900 photons/MeV) and lower detection limits (380 nGy·s-1 at λem = 430 nm and 285 nGy·s-1 at λem = 520 nm). Both phosphors were also embedded in PMMA to create thin films for X-ray imaging, achieving high-resolution outputs (14 lp·mm-1).

DESAT: A Distance-Enhanced Strip Attention Transformer for Remote Sensing Image Super-Resolution

Transformer-based methods have demonstrated impressive performance in image super-resolution tasks. However, when applied to large-scale Earth observation images, the existing transformers encounter two significant challenges: (1) insufficient consideration of spatial correlation between adjacent ground objects; and (2) performance bottlenecks due to the underutilization of the upsample module. To address these issues, we propose a novel distance-enhanced strip attention transformer (DESAT). The DESAT integrates distance priors, easily obtainable from remote sensing images, into the strip window self-attention mechanism to capture spatial correlations more effectively. To further enhance the transfer of deep features into high-resolution outputs, we designed an attention-enhanced upsample block, which combines the pixel shuffle layer with an attention-based upsample branch implemented through the overlapping window self-attention mechanism. Additionally, to better simulate real-world scenarios, we constructed a new cross-sensor super-resolution dataset using Gaofen-6 satellite imagery. Extensive experiments on both simulated and real-world remote sensing datasets demonstrate that the DESAT outperforms state-of-the-art models by up to 1.17 dB along with superior qualitative results. Furthermore, the DESAT achieves more competitive performance in real-world tasks, effectively balancing spatial detail reconstruction and spectral transform, making it highly suitable for practical remote sensing super-resolution applications.

RSDiff: remote sensing image generation from text using diffusion model

The generation and enhancement of satellite imagery are critical in remote sensing, requiring high-quality, detailed images for accurate analysis. This research introduces a two-stage diffusion model methodology for synthesizing high-resolution satellite images from textual prompts. The pipeline comprises a low-resolution diffusion model (LRDM) that generates initial images based on text inputs and a super-resolution diffusion model (SRDM) that refines these images into high-resolution outputs. The LRDM merges text and image embeddings within a shared latent space, capturing essential scene content and structure. The SRDM then enhances these images, focusing on spatial features and visual clarity. Experiments conducted using the Remote Sensing Image Captioning Dataset demonstrate that our method outperforms existing models, producing satellite images with accurate geographical details and improved spatial resolution.

ControlMat: A Controlled Generative Approach to Material Capture

Material reconstruction from a photograph is a key component of 3D content creation democratization. We propose to formulate this ill-posed problem as a controlled synthesis one, leveraging the recent progress in generative deep networks. We present ControlMat, a method which, given a single photograph with uncontrolled illumination as input, conditions a diffusion model to generate plausible, tileable, high-resolution physically-based digital materials. We carefully analyze the behavior of diffusion models for multi-channel outputs, adapt the sampling process to fuse multi-scale information and introduce rolled diffusion to enable both tileability and patched diffusion for high-resolution outputs. Our generative approach further permits exploration of a variety of materials that could correspond to the input image, mitigating the unknown lighting conditions. We show that our approach outperforms recent inference and latent-space optimization methods, and we carefully validate our diffusion process design choices. 1

Enhancing subsurface seismic profiling with distributed acoustic sensing and optimization algorithms

The distribution of shear-wave velocities in the subsurface is generally used to assess the potential for seismic liquefaction and soil amplification effects and to classify seismic sites. Newly developed distributed acoustic sensing (DAS) technology enables estimation of the shear-wave distribution as a high-density seismic observation system. This technology is characterized by low maintenance costs, high-resolution outputs, and real-time data transmission capabilities, albeit with the challenge of managing massive data generation. Rapid and efficient interpretation of data is the key to advancing application of the DAS technology. In this study, field tests were carried out to record ambient noise over a short period using DAS technology, from which the surface-wave dispersion curves were extracted. In order to reduce the influence of directional effects on the results, an unsupervised clustering method is used to select appropriate clusters to extract the Green's function. A combination of a genetic algorithm and Monte Carlo (GA-MC) simulation is proposed to invert the subsurface velocity structure. The stratigraphic profiles obtained by the GA-MC method are in agreement with the borehole profiles. Compared to other methods, the proposed optimization method not only improves the solution quality but also reduces the solution time.

Mathematical modeling and implementation of a panoramic zoom system without central blind area

The panoramic annular lens (PAL) is renowned for providing simultaneous vision of the panoramic space. However, its inherent central blind area is a significant drawback. In this study, we aim to harness the benefits of the PAL while mitigating its limitations by proposing a dual-channel panoramic zoom system without a central blind area. The introduction of a positive front lens group for secondary imaging is a critical aspect of our design, which is guided and substantiated by our mathematical imaging model. The results from our design affirm the practicality and effectiveness of the secondary imaging. This design can achieve a zoom ratio of 1.46× and a maximum field of view (FoV) range of 71°∼100°, enabling zooming from front to rear. Exemplifying compactness, our system measures just 60mm in total length, while providing a high-resolution output, as indicated by a modulation transfer function exceeding 0.3 at 200lp/mm. With an F−θ distortion of less than 3.3%, the system maintains low levels of distortion. The analysis is instructive and the design shows potential in situations that necessitate compliance with size and resolution requirements.

Evaluation of an Impulse-Response Emulator for Groundwater Contaminant Transport Modeling.

There is a significant need to develop decision support tools capable of delivering accurate representations of environmental conditions, such as ground and surface water solute concentrations, in a timely and computationally efficient manner. Such tools can be leveraged to assess a large number of potential management strategies for mitigating non-point source pollutants. Here, we assess the effectiveness of the impulse-response emulation approach to approximate process-based groundwater model estimates of solute transport from MODFLOW and MT3D over a wide range of model inputs and parameters, with the goal of assessing where in parameter space the assumptions underlying this emulation approach are valid. The impulse-response emulator was developed using the sensitivity analysis utilities in the PEST++ software suite and is capable of approximating MODFLOW/MT3D estimates of solute transport over a large portion of the parameter space tested, except in cases where the Courant number is above 0.5. Across all runs tested, the highest percent errors were at the plume fronts. These results suggest that the impulse-response approach may be suitable for emulation of solute transport models for a wide range of cases, except when high-resolution outputs are needed, or when very low concentrations at plume edges are of particular interest.

A dual-mode stick-slip piezoelectric actuator imitating mantis forefoot

Most existing stick-slip piezoelectric actuators fail to achieve smooth motion and linear speed, and the backward motion cannot be actively inhibited during motion. This paper develops a novel piezoelectric actuator with stiffness-variable flexible mechanism imitating mantis forefoot, which utilizes the elastic potential energy to suppress backward motion. Furthermore, the contact condition between the driving foot and the rotor can be adjusted through a joint-like flexible structure. On this basis, contact precisely adjusted driving (CPAD) mode and triangular wave cooperative driving (TWCD) mode are proposed to achieve complete suppression of backward motion, as well as high-speed and high-resolution output. The transfer matrix method (TMM) in the cross-section of multiple state nodes has been improved and applied to accurately establish the theoretical model for the complex flexible hinge structure, with the combination of FEM for structural optimization design and mechanical properties analysis. The error between the calculated results and the experimental results is within 4%. Finally, a prototype with dimensions of Ø36 × 10 mm is manufactured and tested. The results show that both driving modes are able to achieve smooth motion output by actively suppressing backward motion, and a linear speed curve can be achieved under 800 Hz. The prototype achieves a maximum speed of 4491.38 mrad/s and a rotational resolution of 0.332 μrad, which is significantly better than existing actuators with similar size. This study shows great value for applications in the field of fast and precise positioning with size constraints.

Deep Learning for Strain Field Customization in Bioreactor with Dielectric Elastomer Actuator Array.

In the field of biomechanics, customizing complex strain fields according to specific requirements poses an important challenge for bioreactor technology, primarily due to the intricate coupling and nonlinear actuation of actuator arrays, which complicates the precise control of strain fields. This paper introduces a bioreactor designed with a 9×9 array of independently controllable dielectric elastomer actuators (DEAs), addressing this challenge. We employ image regression-based machine learning for both replicating target strain fields through inverse control and rapidly predicting feasible strain fields generated by the bioreactor in response to control inputs via forward control. To generate training data, a finite element analysis (FEA) simulation model was developed. In the FEA, the device was prestretched, followed by the random assignment of voltages to each pixel, yielding 10,000 distinct output strain field images for the training set. For inverse control, a multilayer perceptron (MLP) is utilized to predict control inputs from images, whereas, for forward control, MLP maps control inputs to low-resolution images, which are then upscaled to high-resolution outputs through a super-resolution generative adversarial network (SRGAN). Demonstrations include inputting biomechanically significant strain fields, where the method successfully replicated the intended fields. Additionally, by using various tumor-stroma interfaces as inputs, the bioreactor demonstrated its ability to customize strain fields accordingly, showcasing its potential as an advanced testbed for tumor biomechanics research.

Continuous Pulse Density Modulation of Series Resonant Converter for Wide-Range High-Resolution Output Voltage and High Efficiency

Continuous Pulse Density Modulation of Series Resonant Converter for Wide-Range High-Resolution Output Voltage and High Efficiency

Crafting Expressive Faces: A Comparative Exploration of Facial Animation Techniques in VToonify and AnimeGan

Abstract: This review paper presents an overview and comparative analysis of two cutting-edge technologies: VToonify and AnimeGAN, designed to enhance portrait video style transfer and photo animation, respectively. VToonify introduces a novel high-resolution portrait video style transfer approach, offering users enhanced control over the transformation process. On the other hand, AnimenGAN is a lightweight Generative Adversarial Network (GAN) designed specifically for photo animation with a focus on generating anime-style outputs. This review delves into the underlying methodologies and technical principles employed by both VToonify and AnimeGAN. We discuss the crucial features that set these approaches apart from traditional methods and their respective strengths in terms of controllability, high-resolution output, and efficiency. Furthermore, the paper investigates each technology's key challenges and potential areas for future improvements. The review highlights the practical applications of VToonify and AnimeGAN in the realm of creative content generation, multimedia, and visual storytelling. Moreover, we explore real-world use cases and evaluate the impact of these technologies on various industries, including entertainment, advertising, and social media, Through a comprehensive analysis, this paper aims to provide readers with an informed understanding of the state-of-the-art in portrait video style transfer and photo animation. By combining insights from VToonify and AnimeGAN, this review contributes to advancing research in computer vision, deep learning, and artistic content creation.

The development of a generative approach for joint super-resolution image reconstruction from highly sparse raw data in the context of MR-PET imaging

The present study introduces a rapid and efficient approach for reconstructing high-resolution images in hybrid MRI-PET scanners. The application of sparsity, compressed sensing (CS), and super-resolution reconstruction (SRR) methodologies can significantly decrease the demands of data acquisition while concurrently attaining high-resolution output. G-guided generative multilevel networks for sparsely sampled MR-PET input are shown here. Compressed Sensing using conjugate symmetry and Partial Fourier methodology speeds up data collection over k-space sampling methods. GANs and k-space adjustments are used in this image domain technique. The employed methodology utilizes discrete preprocessing stages to effectively tackle the challenges associated with the deblurring, reducing motion artifacts, and denoising of layers. Initial trials offer contextual details and accelerate evaluations. Preliminary experiments provide contextual information and expedite assessments.

A Triple-Double Convolutional Neural Network for Panchromatic Sharpening.

Pansharpening refers to the fusion of a panchromatic (PAN) image with a high spatial resolution and a multispectral (MS) image with a low spatial resolution, aiming to obtain a high spatial resolution MS (HRMS) image. In this article, we propose a novel deep neural network architecture with level-domain-based loss function for pansharpening by taking into account the following double-type structures, i.e., double-level, double-branch, and double-direction, called as triple-double network (TDNet). By using the structure of TDNet, the spatial details of the PAN image can be fully exploited and utilized to progressively inject into the low spatial resolution MS (LRMS) image, thus yielding the high spatial resolution output. The specific network design is motivated by the physical formula of the traditional multi-resolution analysis (MRA) methods. Hence, an effective MRA fusion module is also integrated into the TDNet. Besides, we adopt a few ResNet blocks and some multi-scale convolution kernels to deepen and widen the network to effectively enhance the feature extraction and the robustness of the proposed TDNet. Extensive experiments on reduced- and full-resolution datasets acquired by WorldView-3, QuickBird, and GaoFen-2 sensors demonstrate the superiority of the proposed TDNet compared with some recent state-of-the-art pansharpening approaches. An ablation study has also corroborated the effectiveness of the proposed approach. The code is available at https://github.com/liangjiandeng/TDNet.

Fusing bi-directional global–local features for single image super-resolution

Image super resolution, which obtains high resolution output from a corresponding low resolution image, has been challenging due to inefficiencies in establishing complex high dimensional mapping for massive raw data. Single image super resolution can dramatically improve performance compared with current algorithms due to the proliferation of deep learning systems. However, convolutional kernels in deep neural networks are locally connected to the input feature maps, whereas features only interact with their local neighbors. Mutual interference between local features without considering global features causes blurring and staircase effects. This paper proposes an end-to-end single image super resolution model by simultaneously separating high and low frequency features and learning adaptive local and global features to effectively reconstruct the high resolution image by minimizing the loss of edges and texture information. The image frequency decomposition module with an attention block emphasizes self-representative low frequency features to separate high and low frequency features. The bidirectional global and local feature exchange module extracts global and local features from the separated network and fuses each feature to improve performance. Quantitative and qualitative analyses for the proposed frequency adaptive network validated that the proposed method is stable and robust against blurring and staircase effects by separating texture and the structure into adaptive and shared networks.

The skill of statistical downscaling in future climate with high-resolution climate models as pseudo-reality

Study regionBelgium Study focusWe assessed statistical downscaling (21 methods) skill and assumptions for seven extreme rainfall indicators in three Belgian areas. The ensemble consists of (i) perturbation methods with four members, (ii) quantile mapping with ten members, and (iii) machine learning with seven members. The methods were evaluated by a perfect predictor experiment in which the outputs of a high-resolution climate model are considered pseudo-observations. The ALARO-0 model with 4 km spatial resolution was considered the high-resolution climate model. The high-resolution outputs were compared with the downscaled results of three future scenarios of the 50 km resolution ALARO-0 model. New hydrological insightsThe methods’ skill depends on the area. The skill is higher for the Belgian coastal area compared to the inland and hilly areas. Methods which are based on stationary assumptions have a good skill for the coastal area but show a lower skill for the other areas where the temporal variability in meteorological conditions is stronger. The higher this temporal variability, the more prone are the stationary assumptions to deteriorate in time under different climate forcing conditions. The quantile-based perturbation methods are overall the most robust. An ensemble approach is advised, where different downscaling methods are applied and the uncertainty in the downscaling taken into account, especially when a pseudo-future evaluation, as done in this study, is not possible.

TCDetect: A New Method of Detecting the Presence of Tropical Cyclones Using Deep Learning

Abstract Tropical cyclones are high-impact weather events that have large human and economic effects, so it is important to be able to understand how their location, frequency, and structure might change in a future climate. Here, a lightweight deep learning model is presented that is intended for detecting the presence or absence of tropical cyclones during the execution of numerical simulations for use in an online data reduction method. This will help to avoid saving vast amounts of data for analysis after the simulation is complete. With run-time detection, it might be possible to reduce the need for some of the high-frequency high-resolution output that would otherwise be required. The model was trained on ERA-Interim reanalysis data from 1979 to 2017, and the training was concentrated on delivering the highest possible recall rate (successful detection of cyclones) while rejecting enough data to make a difference in outputs. When tested using data from the two subsequent years, the recall or probability of detection rate was 92%. The precision rate or success ratio obtained was that of 36%. For the desired data reduction application, if the desired target included all tropical cyclone events, even those that did not obtain hurricane-strength status, the effective precision was 85%. The recall rate and the area under curve for the precision–recall (AUC-PR) compare favorably with other methods of cyclone identification while using the smallest number of parameters for both training and inference.

Learning Continuous Depth Representation via Geometric Spatial Aggregator

Depth map super-resolution (DSR) has been a fundamental task for 3D computer vision. While arbitrary scale DSR is a more realistic setting in this scenario, previous approaches predominantly suffer from the issue of inefficient real-numbered scale upsampling. To explicitly address this issue, we propose a novel continuous depth representation for DSR. The heart of this representation is our proposed Geometric Spatial Aggregator (GSA), which exploits a distance field modulated by arbitrarily upsampled target gridding, through which the geometric information is explicitly introduced into feature aggregation and target generation. Furthermore, bricking with GSA, we present a transformer-style backbone named GeoDSR, which possesses a principled way to construct the functional mapping between local coordinates and the high-resolution output results, empowering our model with the advantage of arbitrary shape transformation ready to help diverse zooming demand. Extensive experimental results on standard depth map benchmarks, e.g., NYU v2, have demonstrated that the proposed framework achieves significant restoration gain in arbitrary scale depth map super-resolution compared with the prior art. Our codes are available at https://github.com/nana01219/GeoDSR.

Degradation learning and Skip-Transformer for blind face restoration

Blindrestoration of low-quality faces in the real world has advanced rapidly in recent years. The rich and diverse priors encapsulated by pre-trained face GAN have demonstrated their effectiveness in reconstructing high-quality faces from low-quality observations in the real world. However, the modeling of degradation in real-world face images remains poorly understood, affecting the property of generalization of existing methods. Inspired by the success of pre-trained models and transformers in recent years, we propose to solve the problem of blind restoration by jointly exploiting their power for degradation and prior learning, respectively. On the one hand, we train a two-generator architecture for degradation learning to transfer the style of low-quality real-world faces to the high-resolution output of pre-trained StyleGAN. On the other hand, we present a hybrid architecture, called Skip-Transformer (ST), which combines transformer encoder modules with a pre-trained StyleGAN-based decoder using skip layers. Such a hybrid design is innovative in that it represents the first attempt to jointly exploit the global attention mechanism of the transformer and pre-trained StyleGAN-based generative facial priors. We have compared our DL-ST model with the latest three benchmarks for blind image restoration (DFDNet, PSFRGAN, and GFP-GAN). Our experimental results have shown that this work outperforms all other competing methods, both subjectively and objectively (as measured by the Fréchet Inception Distance and NIQE metrics).

Identifying spatiotemporal propagation of droughts in the agro-pastoral ecotone of northern China with long-term WRF simulations

Understanding the spatiotemporal evolution of droughts is critical for food security and water allocation. In this study, we propose an approach to construct the linkage of the propagation from meteorological drought to agricultural drought to explore the simultaneous spatiotemporal evolution of droughts based on a 3-dimensional clustering algorithm. We first evaluate the improvement of the downscaled high-resolution outputs from the Weather Research and Forecasting (WRF) model to reanalysis data in detecting wet-dry variations in the agro-pastoral ecotone of northern China (APENC). The WRF simulation results well characterize the evolution of droughts with a high correlation coefficient of 0.82 (p<0.05), which implies that the WRF downscaling model provides reliable hydrometeorological results for assessing and analyzing long-term wet-dry variations. Subsequently, we identify and track the spatiotemporal evolution of each individual drought event in the APENC for the first time. Based on the standardized precipitation index calculated from the WRF outputs, a total of 185 drought clusters were identified during 2000–2017, with 28 drought events lasting at least 3 months duration occurring in the APENC. The characteristics of the different drought events vary considerably. Droughts in the APENC have a larger percentage of propagation to the northeast and northwest and migrated hundreds of kilometers from the source sites. The results of the propagation of meteorological droughts to agricultural droughts indicate that 53.75% of the meteorological drought events progressed further to agricultural droughts. Agricultural droughts exhibit spatial variations consistent with meteorological droughts. Agricultural droughts have 1.69 months onset lag time, 2.12 months termination lag time, and overall longer duration than meteorological droughts in the APENC. Our findings provide valuable information for the mechanism and prediction of drought propagation.

Constrained high-resolution projection of hot extremes in the Beijing–Tianjin–Hebei region of China

The Beijing–Tianjin–Hebei (BTH) region is the political center and one of the largest and most dynamic economic centers in China. Relative to other regions of China, it faces greater challenges from increasing hot extremes. However, future projections of regional changes in hot extremes based on multi-model ensembles carry huge uncertainty. To improve the reliability of regional projections, we used the latest high-resolution outputs from nine GCMs in CMIP6-HighResMIP and constrained the projection of hot extremes in this region. In the BTH, the historical scaling of the annual maximum temperature with the mean summer (June–August) temperatures (TXx scaling) showed a significant linear relationship with the future TXx scaling in the multi-model ensemble. By comparing the observed and simulated historical TXx scaling we identified an observational constraint that could reduce the uncertainty of TXx scaling. We believed that the simulations of EC-Earth3P, EC-Earth3P-HR, MPI-ESM1-2-HR and MPI-ESM1-2-XR show advantages in simulating surface air temperature and related hot extremes in this region. Verification demonstrated that surface air temperature projection in the BTH region constrained by observational constraint is more reliable. In constrained projections, by the 2040s, the increase in summer surface air temperatures is projected to exceed 1 °C compared to 2010s. Also, the estimated number of days of compound (sequential hot day-night) hot extremes (HND), independent hot nights (HNi) and independent hot days (HDi) was found to increase by 0.3, 0.3, and −0.03 d per year during 2015–2049, respectively. The growth in numbers of days of HND, HNi and HDi was slower after observational constraint. Furthermore, the estimated increase in surface air temperature variables after constraint also reduced. This study provides support for adaptation policy-making and as a reference for the use of observational constraint in other regions of China.