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Comparison of Two Spatiotemporal reconstruction Methods for Spaceborne Sea Surface Temperature Data at Multiple Temporal Resolutions

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 16289-16305页

作者： Ma, Xuehua He, Junyu He, Shuangyan Gu, Yanzhen Cao, Anzhou Li, Peiliang Zhou, Feng Zhejiang Univ Ocean Coll Zhoushan 316021 Peoples R China Zhejiang Univ Ocean Acad Joint Ctr Blue Carbon Res Zhoushan 316021 Peoples R China Zhejiang Univ Ocean Res Ctr Zhoushan Zhoushan 316021 Peoples R China Zhejiang Univ Hainan Inst Sanya 572024 Peoples R China Minist Nat Resources Inst Oceanog 2 State Key Lab Satellite Ocean Environm Dynam Hangzhou 310012 Peoples R China Minist Nat Resources Observat & Res Stn Yangtze River Delta Marine Ecos Zhoushan 316022 Peoples R China

The satellite remote sensing sea surface temperature (SST) plays a crucial role in global climate change and ocean-atmosphere interactions. With a notably severe issue of missing data due to clouds and rainfall, data reconstruction methods have been developed to effectively enhance the spatiotemporal completeness of satellite-derived SST data products in recent years. However, few studies have focused on performance comparisons between these different data reconstruction methods, which limits further improvement and application of reconstructed data products. In this study, two representative methods, the data interpolating empirical orthogonal functions (DINEOF) and a spatiotemporal geostatistical method of Bayesian maximum entropy (BME), were used to reconstruct satellite SST data in four regions, and their reconstruction performance under various temporal resolutions (hourly, daily, and monthly) and missing data rates were evaluated and compared. Our results demonstrate that BME consistently outperforms DINEOF. As the missing data rate increases from 10% to 90%, especially when it exceeds 70%, DINEOF reconstruction results exhibit significant increasing noises and reconstruction errors, while BME demonstrates stable precise reconstruction results. Compared with DINEOF method, the results of BME method are less influenced by missing data rates, spatiotemporal resolutions, temporal length, and regions of input data series by different remote sensing sensors, rendering it more applicable and robust in reconstructing multisensor SST data with different temporal resolutions. The BME method holds promising implications in reconstructing high-quality gap-filled data using noisy and high-missing-rate multisensor data in regional areas with high dynamics.

关键词： Spatiotemporal phenomena Oceans Spatial resolution Satellite broadcasting reconstruction algorithms Remote sensing Image reconstruction Bayes methods Entropy Sea surface temperature Climate change Bayesian maximum entropy (BME) data interpolating empirical orthogonal function (DINEOF) data reconstruction sea surface temperature (SST)

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Volumetric Patch-Based Super-Resolution reconstruction of Hyperpolarized ¹³C Cardiac MRI

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IEEE ACCESS 2024年 12卷 164315-164324页

作者： Lin, Sung-Han Ma, Junjie Park, Jae Mo Univ Texas Southwestern Med Ctr Dallas TX 75390 USA GE Healthcare Jersey City NJ 07302 USA

The achievable spatial resolution of C-13 metabolic images acquired with hyperpolarized C-13-pyruvate is worse than 1H images typically by an order of magnitude due to the rapidly decaying hyperpolarized signals and the low gyromagnetic ratio of C-13. This study is to develop and characterize a volumetric patch-based super-resolution reconstruction algorithm that enhances spatial resolution C-13 cardiac MRI by utilizing structural information from 1H MRI. The reconstruction procedure comprises anatomical segmentation from high-resolution 1H MRI, calculation of a patch-based weight matrix, and iterative reconstruction of high-resolution multi-slice C-13 MRI. The method was tested with a multi-compartmental digital phantom for optimizing the patch size and an anthropomorphic cardiac MR phantom for validating the performance. Finally, the method was applied to human cardiac C-13 images, acquired with an injection of hyperpolarized [1-C-13]pyruvate. The phantom studies demonstrated that high-resolution multi-slice C-13 images, reconstructed from a single-slice low-resolution input C-13 image, retained the signal intensity range. The reconstruction accuracy was asymptotically improved as the patch size increased whereas intra-segmental spatial fluctuations were preserved better with smaller patches. However, a structurally non-identified tissue region was not restored regardless of the patch size. The cardiac MR phantom and the human cardiac images demonstrated improved spatial resolutions in the reconstructed images (10 x 10 x 30 mm(3)/voxel to 2 x 2 x 5 mm(3)/voxel). The volumetric patch-based super-resolution method reconstructs multi-slice high-resolution of C-13 images, enhancing the cardiac structure, while preserving the quantitative accuracy. The proposed method is applicable to other multi-modal images that suffer from limited spatial resolution.

关键词： Image reconstruction Magnetic resonance imaging Spatial resolution Superresolution Phantoms Myocardium Image segmentation Lesions Three-dimensional displays reconstruction algorithms Cardiovascular system reconstruction algorithm image resolution magnetic resonance imaging metabolism

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A 3-D Track reconstruction Algorithm for Preresearch of the STCF MDC L1 Trigger

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2025年第3期72卷 429-437页

作者： Hao, Yidi Feng, Changqing Zhou, Zixuan Dong, Wenhao Fang, Zhujun Hu, Xueye Zhou, Hang Liu, Shubin Univ Sci & Technol China State Key Lab Particle Detect & Elect Hefei 230026 Peoples R China Univ Sci & Technol China Dept Modern Phys Hefei 230026 Peoples R China

The proposed Super Tau Charm Facility (STCF) is an electron-positron collider with high luminosity. Under the conditions of a high radiation, high counting-rate environment, and a pure physics event rate of up to 400 kHz, for a Level 1 (L1) trigger system, it is crucial to suppress background events to an acceptable rate. To this end, this study presents a 3-D track reconstruction algorithm for the preresearch of the STCF main drift chamber (MDC) L1 trigger. The proposed algorithm can reject tracks outside the interaction region and provide 3-D track information to the global trigger logic (GTL) of an L1 trigger for further analysis with other subdetectors. By using hit information from the MDC and the transverse momentum ( p(T)) and azimuthal angle (phi) from previous research on 2-D reconstruction, this study reconstructs the z (longitudinal) position for the track vertex using a neural network-based method with an innovative stereo track segment (TS) design. The neural network is trained using high granularity quantization (HGQ) to reduce resource consumption while maintaining resolution. The proposed method is implemented into a field-programmable gate array (FPGA) using the hls4ml. The achieved resolution of the z-vertex reconstruction of a single track ( z is an element of [-50, 50] cm) is approximately 2.8 cm, which can ensure rejecting 97% beam background tracks in a +/- 3 sigma interval.

关键词： Wires Three-dimensional displays reconstruction algorithms Detectors Physics Data acquisition Neural networks Approximation algorithms Monte Carlo methods Logic Hls4ml Level 1 (L1) trigger algorithm main drift chamber (MDC) multilayer perceptron (MLP) neural network Super Tau Charm Facility (STCF)

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Spectral reconstruction for Internet of Things Based on Parallel Fusion of CNN and Transformer

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IEEE INTERNET OF THINGS JOURNAL 2025年第4期12卷 3549-3562页

作者： Sun, Bangyong Wu, Changyu Yu, Mengying Xian Univ Technol Sch Printing Packaging & Digital Media Xian 710048 Peoples R China

Spectral imaging acquires more analyzable and distinguishable information than RGB imaging and has become an emerging technique powering Internet of Things (IoT). Thus, generating spectral images from existing RGB cameras is instrumental to high-level vision tasks. In this article, an IoT-oriented spectral reconstruction model with parallel fusion of convolutional neural network (CNN) and transformer (PFCT) is proposed to efficiently recover hyperspectral images (HSIs) from RGB counterparts, facilitating low-cost and nonhardware-specific spectral image acquisition. Recent works mainly utilize CNNs to extract local features by stacking more layers, ignoring the latent correlations of global features. In our PFCT network, we take advantage of lightweight CNNs to efficiently perceive local details, and exploit transformer blocks to fully capture the global context. Based on the architecture, a parallel fusion module is further designed to deeply interact and fuse the features obtained by CNN and Transformer in both directions. The final output spectral image is generated with same spatial sizes of the original image based on the deeply fused features. Consequently, the proposed PFCT network achieved high performance on four benchmark data sets compared to several state-of-the-art networks with a relatively small number of parameters.

关键词： Image reconstruction Hyperspectral imaging Convolutional neural networks Feature extraction Transformers Imaging reconstruction algorithms Convolutional neural network (CNN) Internet of Things (IoT) spectral reconstruction transformer

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3D Helical CT reconstruction With a Memory Efficient Learned Primal-Dual Architecture

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2024年 10卷 1414-1424页

作者： Rudzusika, Jevgenija Bajic, Buda Koehler, Thomas Oktem, Ozan KTH Royal Inst Technol Dept Math S-11428 Stockholm Sweden Univ Novi Sad Fac Tech Sci Novi Sad 21000 Serbia Philips Innovat Technol D-22335 Hamburg Germany

Deep learning based computed tomography (CT) reconstruction has demonstrated outstanding performance on simulated 2D low-dose CT data. This applies in particular to domain adapted neural networks, which incorporate a handcrafted physics model for CT imaging. Empirical evidence shows that employing such architectures reduces the demand for training data and improves upon generalization. However, their training requires large computational resources that quickly become prohibitive in 3D helical CT, which is the most common acquisition geometry used for medical imaging. This paper modifies a domain adapted neural network architecture, the Learned Primal-Dual (LPD), so that it can be trained and applied to reconstruction in this setting. The main challenge is to reduce the GPU memory requirements during the training, while keeping the computational time within practical limits. Furthermore, clinical data also comes with other challenges not accounted for in simulations, like errors in flux measurement, resolution mismatch and, most importantly, the absence of the real ground truth. To the best of our knowledge, this work is the first to apply an unrolled deep learning architecture for reconstruction on full-sized clinical data, like those in the Low dose CT image and projection data set (LDCT).

关键词： Computed tomography Image reconstruction Computer architecture Training Three-dimensional displays Neural networks reconstruction algorithms deep learning helical acquisition primal-dual clinical data

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Communication Topology reconstruction for a Three-Dimensional Persistent Formation With Fault Constraint

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第6期11卷 6574-6588页

作者： Wang, Guoqiang Luo, He Hu, Xiaoxuan Hefei Univ Technol Sch Management Hefei 230009 Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei 230009 Peoples R China Engn Res Ctr Intelligent Management Aerosp Syst Hefei 230009 Anhui Peoples R China

The aim of communication topology reconstruction of a persistent formation with fault constraint is to reconstruct the communication topology in a timely manner in the case of a fault to ensure that the agents continue to maintain the formation shape and to minimize the communication energy consumption among agents. The formation communication cost of solutions obtained by existing algorithms is high, and their calculation time is long. To address this problem, a novel communication topology reconstruction algorithm for persistent formations in three-dimensional space is proposed based on arc addition and path reversal operations. Then, the correctness of the algorithm is proved theoretically, and its scalability for two-dimensional space is also analyzed. Finally, its effectiveness is verified through numerical experiments. Compared with existing algorithms, its advantages regarding solution quality and calculation time increase with the increase in the number of agents and the proportion of link loss in the faults.

关键词： Topology Network topology Shape Costs Optimization reconstruction algorithms Energy consumption Agent loss communication topology link loss multi-agent systems optimally persistent graph persistent formation topology optimization

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Phase Current reconstruction and Suppression in a Single Current Sensor Measurement-Based Four-Phase SRM-Integrated Drive System

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Cai, Jun Liu, Zeyuan Song, Shoujun Nanjing Univ Informat Sci & Technol C MEIC CICAEET Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China Anhui Jianzhu Univ Sch Mech & Elect Engn Hefei 230009 Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210042 Peoples R China Northwestern Polytech Univ Coll Automat Xian 710072 Peoples R China

In this article, a compact single current sensor measurement-based four-phase switched reluctance motor (SRM)-integrated drive system is developed. Based on the current measurement characteristics of this drive system, the phase current reconstruction and demagnetization current suppression strategies are proposed. To ensure reliable operation of the SRM, two key issues are considered: 1) the four-phase current should be obtained from the only measured bus current signal and 2) as the nonconducting phases are unable to demagnetize normally with traditional control algorithms, serious negative torque effects exist. For the first issue, a four-phase current reconstruction method is proposed by decoupling the adjacent two-phase currents in the overlapping conduction regions. For the second issue, high-frequency pulses are injected in the shared upper power switch of the bridge arm where the conducting phase is located, providing rapid demagnetization for the other phase in the same bridge arm. The two methods are fully decoupled and can be integrated freely to achieve current reconstruction and demagnetization of current suppression. Based on these methods, a hybrid control algorithm that can satisfy the operation requirement in different speed ranges is developed for the SRM drive. Experiments have been implemented in an 8/6 structure SRM prototype-based system, and detailed experimental results verify the effectiveness of the proposed methods.

关键词： Current measurement Demagnetization Topology Phase measurement Power measurement Electronic mail Accuracy Torque Switched reluctance motors reconstruction algorithms Demagnetization current suppression integrated power converter phase current reconstruction single current sensor measurement switched reluctance motor (SRM)

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Phase Current reconstruction for DTP-PMSM With Single Current Sensor Using Synchronous Filtering reconstruction Method

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2025年第6期40卷 8528-8539页

作者： Zhao, Wenxiang Ma, Sen Tao, Tao Wu, Yunjiang Ling, Jincheng Wang, Huanan Jiangsu Univ Sch Elect Informat Engn Zhenjiang 212013 Peoples R China Nanjing Inst Technol Sch Elect Power Engn Nanjing 211167 Peoples R China

In dual three-phase permanent magnet synchronous motor (DTP-PMSM) drive, six-phase current information is critical to motor control. However, the adoption of multiple current sensors increases the size and cost of inverters. To mitigate this issue, this article proposes a method to reconstruct six-phase currents of a DTP-PMSM by using a single current sensor (SCS). First, the SCS is repositioned to a current branch. Then, sampling is performed twice in one period based on the switching state of a single bridge arm. Therefore, two orthogonal phase currents are achieved. Furthermore, the synchronous filtering reconstruction method is proposed. Through this method, the harmonic current information is extracted from two orthogonal currents and the remaining four-phase currents are reconstructed. Overall, six-phase currents can be accurately reconstructed by using an SCS, for which the hardware cost and inverter size can be reduced. The proposed method is validated by both simulation and experiments.

关键词： Vectors reconstruction algorithms Harmonic analysis Power harmonic filters Switches Pulse width modulation Current measurement Inverters Filtering Bridge circuits Dual three-phase permanent magnet synchronous motor (DTP-PMSM) phase current reconstruction single current sensor (SCS) synchronous filtering reconstruction

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A Multiscale reconstruction Framework Based on Edge-Enhanced Guidance for In-Line X-Ray Phase-Contrast CT With Limited-Angle Projections

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Li, Yimin Zhao, Yuanyuan Ma, Chenyang Li, Fangzhi Wang, Ziyao Lv, Wenjuan Ji, Dongjiang Jian, Jianbo Zhao, Xinyan Zhao, Yuqing Hu, Chunhong Tianjin Med Univ Sch Biomed Engn & Technol Tianjin 300070 Peoples R China Tianjin Univ Technol & Educ Sch Sci Tianjin 300222 Peoples R China Tianjin Med Univ Gen Hosp Tianjin 300052 Peoples R China Capital Med Univ Beijing Friendship Hosp Liver Res Ctr Beijing 100050 Peoples R China

The in-line X-ray phase-contrast computed tomography (IL-XPCCT) serves as an effective tool for studying organ function and pathologies. However, IL-XPCCT approaches often result in high radiation doses due to long scan times. To address this issue, a limited angel sampling strategy is frequently employed. However, limited-angle projection data can lead to severe divergence phenomena at sample edges or boundaries, significantly reducing image quality and affecting subsequent image analysis. In this work, we propose a novel multiscale reconstruction framework based on edge-enhanced guidance (MSRF-EEG) for IL-XPCCT limited-angle CT reconstruction. The MSRF-EEG framework consists of two primary subnetworks: an edge-enhanced subnetwork (EESN) and a multiscale reconstruction subnetwork (MSRSN). By fully considering the imaging characteristics of the IL-XPCCT, the EESN is designed to explore the edge enhancement characteristics from the IL-XPCCT images prior to phase retrieval, which is then utilized to compensate for edge distortions during the reconstruction process. The MSRSN performs image reconstruction with edge-enhanced guidance across multiple scales simultaneously. Furthermore, to better leverage the edge enhancement characteristics before phase retrieval for reconstructing the IL-XPCCT images from limited-angle projections, an edge aggregation (EAG) module and an edge attention (EAT) module are incorporated into the MSRSN. Simulations and real data experiments are conducted to evaluate the performance of our proposed MSRF-EEG framework. Compared with the existing competitive algorithms, both quantitative and qualitative results demonstrate that the proposed method can significantly enhance reconstruction quality under different sampling angles and improve detail restoration and edge preservation.

关键词： Image reconstruction Image edge detection Computed tomography Imaging X-ray imaging TV Detectors Decoding reconstruction algorithms Phase measurement Edge enhancement in-line X-ray phase-contrast computed tomography (IL-XPCCT) limited-angle reconstruction phase retrieval

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SCA-CVENet: A Spatial-Channel-Attention Collaborative Cost Volume Enhancement Network for High-Quality Depth reconstruction

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IEEE ACCESS 2025年 13卷 58264-58280页

作者： Tian, Mao Zhao, Xudong Lv, Xiong Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing 400065 Peoples R China

Accurate and complete depth map prediction from a set of overlapping multi-view stereo images has received extensive attention in the fields of photogrammetry and computer vision. Despite the tremendous efforts made in multi-view depth map reconstruction in recent years, the accuracy and completeness of the reconstructed depth map in matching difficulty areas needs to be further improved. In this paper, to address this issue, we propose SCA-CVENet, which is an efficient spatial-channel-attention collaborative cost volume enhancement network for high-quality depth map reconstruction. The main contributions of SCA-CVENet are the channel attention weight (CAW)-based cost volume enhancement module that can effectively filter the redundant channel information of the feature volumes to boost the feature representation of the cost volumes, and the spatial attention weight (SAW)-based cost volume enhancement module that can significantly improve the robustness of the cost volumes in textureless and repetitive texture areas, to produce more accurate and complete depth map reconstruction results. First, a weight-sharing hierarchical feature extraction subnetwork is adopted to extract robust multi-scale feature maps. Second, the SAW generation subnetwork is adopted, which incorporates the group-wise correlation (GWC)-based cost volume adaptive fusion module and 3D hourglass module to produce an accurate SAW. Finally, the coarse-to-fine cascade depth prediction subnetwork incorporates the CAW- and SAW-based cost volume enhancement modules into the cascade network architecture to predict a high-quality depth map. Three publicly available benchmark datasets-the DTU, BlendedMVS, and Tanks & Temples datasets-were utilized to validate the performance of SCA-CVENet, and the comprehensive experimental results demonstrate that SCA-CVENet obtains a superior performance on all the datasets in terms of accuracy and completeness.

关键词： Costs Image reconstruction Accuracy Three-dimensional displays reconstruction algorithms Robustness Information filters Memory management Feature extraction Collaboration Depth prediction multi-view depth map reconstruction channel attention weight (CAW) spatial attention weight (SAW) cascade network architecture

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