In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effect...In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effectively. Improving the signal resolution is the key to signal time-frequency analysis processing and has wide use in geophysical data processing and extraction of attribute parameters. In this paper, authors research the effects of the attenuation coefficient choice of the Gabor transform window function and sampling interval on signal resolution. Unsuitable parameters not only decrease the signal resolution on the frequency spectrum but also miss the signals. It is essential to first give the optimum window and range of parameters through time-frequency analysis simulation using the Gabor transform. In the paper, the suggestions about the range and choice of the optimum sampling interval and processing methods of general seismic signals are given.展开更多
Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conductin...Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.展开更多
Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(...Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].展开更多
Understanding how rock slopes respond to blasting loads is crucial for maintaining excavation safety and slope stability.Nevertheless,the spatiotemporal evolution,nonlinear dependence on blasting parameters,and predic...Understanding how rock slopes respond to blasting loads is crucial for maintaining excavation safety and slope stability.Nevertheless,the spatiotemporal evolution,nonlinear dependence on blasting parameters,and predictive behavior of dominant frequency responses in slope vibrations remain insufficiently understood and quantified.This study combines time-frequency analysis with machine learning to explore how the dominant frequency(f_(d))evolves in slopes under blasting.Continuous Wavelet Transform(CWT)was employed to characterize the temporal-frequency evolution of vibration signals,revealing that the dominant frequency exhibits strong spatial dependence and nonlinear variability influenced by blasting parameters and rock mass structures.Three machine learning models,namely Back Propagation Neural Network(BP),Support Vector Machine(SVM),and Random Forest(RF),were developed to predict f_(d) based on 1,000 monitoring samples obtained from numerical and field simulations.Among them,the RF model achieved the highest prediction accuracy,with mean absolute percentage errors(MAPE)below 15%,demonstrating strong robustness and generalization capability.Our analysis shows that external excitation factors,especially the loading frequency(f_(d)),mainly control the frequency response,while internal controlling factors,such as spatial position,lithological variation,and mechanical heterogeneity,modulate localized frequency amplification and energy redistribution.The results reveal that f_(d) tends to decrease with elevation and distance from the blasting source,whereas structural planes and weathered zones induce high-frequency amplification due to scattering and modal coupling effects.This study offers a new framework combining time-frequency analysis and machine learning to measure the nonlinear interaction between blasting and rock mass response,offering new insights for dynamic stability evaluation and hazard mitigation in complex rock slope systems.展开更多
The state-of-the-art optical atomic clocks and the time-frequency signal transmission open a fresh field for gravity potential(geopotential)determination.Various methods,including optical fiber frequency transfer,sate...The state-of-the-art optical atomic clocks and the time-frequency signal transmission open a fresh field for gravity potential(geopotential)determination.Various methods,including optical fiber frequency transfer,satellite two-way,satellite common-view,satellite carrier phase,VLBI,tri-frequency combination,and dual-frequency combination,were developed to determine the geopotential differences using optical atomic clocks and then determine the geopotential at station B based on the geopotential at station A.This review elaborates the principles,methods,scientific objectives,applications,and relevant research trends of geopotential determination based on time-frequency signals.展开更多
In complex media scattering,multiple scattering severely degrades the optical wavefront and results in blurred images,while the spectral distortion caused by the scattering effect leads to severe color distortion.Achi...In complex media scattering,multiple scattering severely degrades the optical wavefront and results in blurred images,while the spectral distortion caused by the scattering effect leads to severe color distortion.Achieving color high-resolution imaging through scattering media remains a significant challenge.Here,we propose a broadband,polarization-based method for color high-resolution imaging through scattering media.This approach enables high-resolution reconstruction by effectively separating the speckle illumination pattern from the mixed-scattering field information,leveraging polarization common-mode characteristics.Concurrently,it incorporates chromatic balance compensation to correct spectral aliasing in the scattered light field,enabling color high-resolution imaging through complex scattering media.To further optimize color distortion caused by scattering,a compensation strategy combining color constancy and white balance theory is adopted.Experimental results demonstrate that the proposed method significantly enhances both spatial resolution and color fidelity across various scattering conditions and target materials,showcasing strong adaptability and robustness.This approach provides an effective solution for achieving high-resolution color optical imaging in complex scattering environments.展开更多
The pseudo-two-dimensional(P2D)model plays an important role in exploring physicochemical mechanisms,predicting the state of health,and improving the fast charge capability for Li-ion batteries(LIBs).However,the fast ...The pseudo-two-dimensional(P2D)model plays an important role in exploring physicochemical mechanisms,predicting the state of health,and improving the fast charge capability for Li-ion batteries(LIBs).However,the fast charge leads to the lithium concentration gradient in the solid and electrolyte phases and the non-uniform electrochemical reaction at the solid/electrolyte interface.In order to decouple charge transfer reactions in LIBs under dynamic conditions,understanding the spatio-temporal resolution of the P2D model is urgently required.Till now,the study of this aspect is still insufficient.This work studies the spatio-temporal resolution for dynamic/static electrochemical impedance spectroscopy(DEIS/SEIS)on multiple scales.In detail,DEIS and SEIS with spatio-temporal resolutions are used to decouple charge transfer reactions in LIBs based on the numerical solution of the P2D model in the frequency domain.The calculated results indicate that decoupling solid diffusion requires a high spatial resolution along the r-direction in particles,decoupling electrolyte diffusion and interfacial transfer reaction requires a high spatial resolution along the x-direction,and decoupling charge transfer reactions in LIBs at an extremely low state of charge(SOC)requires an extremely high temporal resolution along the t-direction.Finally,the optimal range of spatio-temporal resolutions for DEIS/SEIS is derived,and the method to decouple charge transfer reactions with spatio-temporal resolutions is developed.展开更多
Nociceptive pain is a cardinal feature of traumatic and inflammatory bone diseases.However,whether and how nociceptors actively regulate the immune response during bone regeneration remains unclear.Here,we found that ...Nociceptive pain is a cardinal feature of traumatic and inflammatory bone diseases.However,whether and how nociceptors actively regulate the immune response during bone regeneration remains unclear.Here,we found that neutrophil-triggered nociceptive ingrowth functioned as negative feedback regulation to inflammation during bone healing.A unique Il4ra^(+)Ccl2^(high) neutrophil subset drove intense postinjury TRPV1^(+)nociceptive ingrowth,which in return dissipated inflammation by activating the production of pro-resolving mediator lipoxin A4(LXA4)in osteoblasts.Mechanistically,osteoblastic autophagy activated by nociceptor-derived calcitonin gene-related peptide(CGRP)suppressed the nuclear translocation of arachidonate 5-lipoxygenase(5-LOX)to favor the LXA4 biosynthesis.Moreover,in alveolar bone from patients with Type Ⅱ diabetes,we found diminished nociceptive innervation correlated with reduced autophagy,increased inflammation,and impaired bone formation.Activating nociceptive nerves by spicy diet or topical administration of a clinical-approved TRPV1 agonist showed therapeutic benefits on alveolar bone healing in diabetic mice.These results reveal a critical neuroimmune interaction underlying the inflammation-regeneration balance during bone repairing and may lead to novel therapeutic strategies for inflammatory bone diseases.展开更多
High-resolution solar observations are critical for resolving small-scale dynamic solar processes.Specifically,solar continuum observations,which are used to characterize the photospheric radiative energy distribution...High-resolution solar observations are critical for resolving small-scale dynamic solar processes.Specifically,solar continuum observations,which are used to characterize the photospheric radiative energy distribution,identify atmospheric temperature gradients,and model space weather events,serve as a cornerstone of solar physics research.However,existing observational frameworks face inherent limitations:space-based instruments are constrained by diffraction limits,while ground-based data suffer from atmospheric turbulence and temporal discontinuity.To address these challenges,this study proposes a resolution enhancement method based on cross-platform data fusion between Solar Dynamics Observatory(SDO)/Helioseismic and Magnetic Imager(HMI)space-based full-disk coverage observations and Optical and Near-infrared Solar Eruption Telescope(ONSET)ground-based high-resolution local observations to overcome the physical limitations faced by single-instrument observations.Using 6537 preprocessed spatiotemporally aligned datasets(from 2022),we achieve sub-pixel registration via the scale-invariant feature transform(SIFT)algorithm and design a lightweight model called Cross-Instrument Super-Resolution(CISR)based on a residual local feature block network,optimized for feature extraction and reconstruction using the smooth L1-loss function.Experimental results demonstrate that CISR achieves a pixel-wise correlation coefficient of 0.946,a peak signal-to-noise ratio(PSNR)of 33.924 dB,and a structural similarity index of 0.855 on the test set,significantly outperforming bicubic interpolation and the Super-Resolution Convolutional Neural Network(SRCNN)baseline model.Qualitative visual assessment verifies the method’s efficacy for HMI continuum data resolution enhancement,with exceptional performance in maintaining both sunspot boundary acuity and granule structural fidelity.This work provides a novel approach for multi-source solar data synergy,with future potential to incorporate physics-driven evaluation metrics to further improve the model generalization.展开更多
A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spa...A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spatial-temporal message passing mechanism built on tensor product.Concretely,an HGCN utilizes the discrete Fourier transform(DFT)to implement temporal message passing and then employs face-wise product to realize spatial message passing.However,DFT is only a special case of assorted time-frequency transforms,which considers the complex temporal patterns partially,thereby resulting in an inaccurate temporal message passing possibly.To address this issue,this study proposes six advanced time-frequency transform-incorporated HGCNs(TF-HGCNs)with discrete Fourier,discrete Hartley,discrete cosine,Haar wavelet,Walsh Hadamard,and slant transforms.In addition,a potent ensemble is built regarding the proposed six TF-HGCNs as the bases.Finally,the corresponding theoretical proof is presented.Empirical studies on six DG datasets demonstrate that owing to diverse time-frequency transforms,the proposed six TF-HGCNs significantly outperform state-of-the-art models in addressing the task of link weight estimation.Moreover,their ensemble outstrips each base's performance.展开更多
To address the challenges of complex fluvial sandbody distribution and difficult remaining oil recovery in mature continental oilfields,this study focuses on key issues in reservoir identification such as ambiguous na...To address the challenges of complex fluvial sandbody distribution and difficult remaining oil recovery in mature continental oilfields,this study focuses on key issues in reservoir identification such as ambiguous narrow-channel boundaries and subdivision of multi-stage superimposed sandbodies.Taking the Upper Cretaceous continental sandstone in the Sazhong Oilfield of the Daqing Placanticline as an example,a technical system integrating OVT high-resolution processing,multi-attribute fusion,and varible-scale inversion was developed to establish a complete workflow from seismic processing to reservoir prediction and remaining oil recovery.The following results are obtained.First,the Offset Vector Tile(OVT)seismic processing technology is extended,for the first time,from fracture imaging to sandbody prediction,in order to address the weak seismic responses from boundaries of narrow and thin sandbodies.A geology-oriented OVT partitioning method is developed to significantly improve the imaging accuracy,enabling identification of channel sandbodies as narrow as 50 m.Second,an amplitude-coherence dual-attribute fusion method is proposed for predicting narrow channel boundaries between wells.Constrained by a sedimentary unit-level sequence chronostratigraphic framework,this method accurately delineates 800-2000 m long subaqueous distributary channels with bifurcation-convergence features.Third,considering the superimposition of multi-stage channels,a three-level variable-scale stratigraphic model(sandstone groups,sublayers,sedimentary units)is constructed to overcome single-scale modeling limitations,successfully characterizing key sedimentary features like meandering river“cut-offs”through 3D seismic inversion.Based on these advances,a direct link between seismic prediction and remaining oil recovery is established.The horizontal wells deployed using narrow-channel predictions encountered oil-bearing sandstones in the horizontal section by 97%,and achieved initial daily production of 12.5 t per well.Precise identification of individual channel boundaries within 17 composite sandbodies guided recovery processes in 135 wells,yielding an average daily increase of 2.8 t per well and a cumulative increase of 13.6×10^(4)t.展开更多
The resolution of seismic data is critical to seismic data processing and the subsequent interpretation of fine structures. In conventional resolution improvement methods, the seismic data is assumed stationary and th...The resolution of seismic data is critical to seismic data processing and the subsequent interpretation of fine structures. In conventional resolution improvement methods, the seismic data is assumed stationary and the noise level not changes with space, whereas the actual situation does not satisfy this assumption, so that results after resolution improvement processing is not up to the expected effect. To solve these problems, we propose a seismic resolution improvement method based on the secondary time-frequency spectrum. First, we propose the secondary time-frequency spectrum based on S transform （ST） and discuss the reflection coefficient sequence and time-dependent wavelet in the secondary time frequency spectrum. Second, using the secondary time frequency spectrum, we design a two- dimensional filter to extract the amplitude spectrum of the time-dependent wavelet. Then, we discuss the improvement of the resolution operator in noisy environments and propose a novel approach for determining the broad frequency range of the resolution operator in the time- fi＇equency-space domain. Finally, we apply the proposed method to synthetic and real data and compare the results of the traditional spectrum-modeling deconvolution and Q compensation method. The results suggest that the proposed method does not need to estimate the Q value and the resolution is not limited by the bandwidth of the source. Thus, the resolution of the seismic data is improved sufficiently based on the signal-to-noise ratio （SNR）.展开更多
Due to the limitations of spatial bandwidth product and data transmission bandwidth,the field of view,resolution,and imaging speed constrain each other in an optical imaging system.Here,a fast-zoom and high-resolution...Due to the limitations of spatial bandwidth product and data transmission bandwidth,the field of view,resolution,and imaging speed constrain each other in an optical imaging system.Here,a fast-zoom and high-resolution sparse compound-eye camera(CEC)based on dual-end collaborative optimization is proposed,which provides a cost-effective way to break through the trade-off among the field of view,resolution,and imaging speed.In the optical end,a sparse CEC based on liquid lenses is designed,which can realize large-field-of-view imaging in real time,and fast zooming within 5 ms.In the computational end,a disturbed degradation model driven super-resolution network(DDMDSR-Net)is proposed to deal with complex image degradation issues in actual imaging situations,achieving high-robustness and high-fidelity resolution enhancement.Based on the proposed dual-end collaborative optimization framework,the angular resolution of the CEC can be enhanced from 71.6"to 26.0",which provides a solution to realize high-resolution imaging for array camera dispensing with high optical hardware complexity and data transmission bandwidth.Experiments verify the advantages of the CEC based on dual-end collaborative optimization in high-fidelity reconstruction of real scene images,kilometer-level long-distance detection,and dynamic imaging and precise recognition of targets of interest.展开更多
The study of the charge conjugation and parity(CP)violation of hyperon is the precision frontier for probing possible new CP violation sources beyond the standard model(SM).With the large number of quantum entangled h...The study of the charge conjugation and parity(CP)violation of hyperon is the precision frontier for probing possible new CP violation sources beyond the standard model(SM).With the large number of quantum entangled hyperonantihyperon pairs to be produced at Super Tau-Charm Facility(STCF),the CP asymmetry of hyperon is expected to be tested with a statistical sensitivity of 10^(−4) or even better.To cope with the statistical precision,the systematic effects from various aspects are critical and need to be studied in detail.In this paper,the sensitivity effects on the CP violation parameters associated with the detector resolution,including those of the position and momentum,are studied and discussed in detail.The results provide valuable guidance for the design of STCF detector.展开更多
The CUG_CLMFM3D series comprises high-resolution three-dimensional lithospheric magnetic field models for China and its surroundings.The first version,CUG_CLMFM3Dv1,is a spherical cap harmonic model integrating the WD...The CUG_CLMFM3D series comprises high-resolution three-dimensional lithospheric magnetic field models for China and its surroundings.The first version,CUG_CLMFM3Dv1,is a spherical cap harmonic model integrating the WDMAMv2(World Digital Magnetic Anomaly Map version 2)global magnetic anomaly grid and nearly a decade of CHAMP(Challenging Minisatellite Payload for Geophysical Research and Application)satellite vector data.It achieves a~5.7 km resolution but has limitations:the WDMAMv2 grid lacks high-resolution data in the southern Xinjiang and Tibet regions,which leads to missing small-to medium-scale anomalies,and unfiltered CHAMP data introduce low-frequency conflicts with global spherical harmonic models.Above the altitude of 150 km,correlations with global models drop below 0.9.The second version,CUG_CLMFM3Dv2,addresses these issues by incorporating 5-km-resolution aeromagnetic data and rigorously processed satellite data from CHAMP,Swarm,CSES-1(China Seismo-Electromagnetic Satellite 1),and MSS-1(Macao Science Satellite 1).The comparison analysis shows that the CUG_CLMFM3Dv2 captures finer high-frequency details and more stable long-wavelength signals,offering improved magnetic anomaly maps for further geological and geophysical studies.展开更多
This article proposes a three-dimensional light field reconstruction method based on neural radiation field(NeRF)called Infrared NeRF for low resolution thermal infrared scenes.Based on the characteristics of the low ...This article proposes a three-dimensional light field reconstruction method based on neural radiation field(NeRF)called Infrared NeRF for low resolution thermal infrared scenes.Based on the characteristics of the low resolution thermal infrared imaging,various optimizations have been carried out to improve the speed and accuracy of thermal infrared 3D reconstruction.Firstly,inspired by Boltzmann's law of thermal radiation,distance is incorporated into the NeRF model for the first time,resulting in a nonlinear propagation of a single ray and a more accurate description of the physical property that infrared radiation intensity decreases with increasing distance.Secondly,in terms of improving inference speed,based on the phenomenon of high and low frequency distribution of foreground and background in infrared images,a multi ray non-uniform light synthesis strategy is proposed to make the model pay more attention to foreground objects in the scene,reduce the distribution of light in the background,and significantly reduce training time without reducing accuracy.In addition,compared to visible light scenes,infrared images only have a single channel,so fewer network parameters are required.Experiments using the same training data and data filtering method showed that,compared to the original NeRF,the improved network achieved an average improvement of 13.8%and 4.62%in PSNR and SSIM,respectively,while an average decreases of 46%in LPIPS.And thanks to the optimization of network layers and data filtering methods,training only takes about 25%of the original method's time to achieve convergence.Finally,for scenes with weak backgrounds,this article improves the inference speed of the model by 4-6 times compared to the original NeRF by limiting the query interval of the model.展开更多
Microsphere and microcylinder-assisted microscopy(MAM)has grown steadily over the last decade and is still an intensively studied optical far-field imaging technique that promises to overcome the fundamental lateral r...Microsphere and microcylinder-assisted microscopy(MAM)has grown steadily over the last decade and is still an intensively studied optical far-field imaging technique that promises to overcome the fundamental lateral resolution limit of microscopy.However,the physical effects leading to resolution enhancement are still frequently debated.In addition,various configurations of MAM operating in transmission mode as well as reflection mode are examined,and the results are sometimes generalized.We present a rigorous simulation model of MAM and introduce a way to quantify the resolution enhancement.The lateral resolution is compared for microscope arrangements in reflection and transmission modes.Furthermore,we discuss different physical effects with respect to their contribution to resolution enhancement.The results indicate that the effects impacting the resolution in MAM strongly depend on the arrangement of the microscope and the measurement object.As a highlight,we outline that evanescent waves in combination with whispering gallery modes also improve the imaging capabilities,enabling super-resolution under certain circumstances.This result is contrary to the conclusions drawn from previous studies,where phase objects have been analyzed,and thus further emphasizes the complexity of the physical mechanisms underlying MAM.展开更多
In February 2025,a startup satellite manufacturer,Albedo(Broomfield,CO,USA)is expected to launch its first satellite,Clarity-1,into orbit aboard SpaceX’s Transporter-13,a Falcon 9 rideshare mission[1].Like many imagi...In February 2025,a startup satellite manufacturer,Albedo(Broomfield,CO,USA)is expected to launch its first satellite,Clarity-1,into orbit aboard SpaceX’s Transporter-13,a Falcon 9 rideshare mission[1].Like many imaging satellites,Clarity-1’s mis-sion will be to take high-resolution aerial photos for clients in var-ious economic sectors including agriculture,insurance,energy,mapping,utilities,and defense.What makes this satellite unique is both its industry-leading 10 cm spatial resolution and its extre-mely low orbit of 200 km,far closer to Earth than the 450 km or higher orbits of most of its peers with similar missions.展开更多
With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communicat...With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communication system.In particular,the abnormal signals may emulate the normal signals,which makes it very challenging for abnormal signal recognition.In this paper,we propose a new abnormal signal recognition scheme,which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals.Firstly,we emulate synthetic abnormal communication signals including seven jamming patterns.Then,we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver.To improve the performance,we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm.Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations,even under low signal-to-noise ratio(SNR)and low jamming-to-signal ratio(JSR)conditions.展开更多
In clinical diagnosis,magnetic resonance imaging(MRI)allows different contrast images to be obtained.High-resolution(HR)MRI presents fine anatomical structures,which is important for improving the efficiency of expert...In clinical diagnosis,magnetic resonance imaging(MRI)allows different contrast images to be obtained.High-resolution(HR)MRI presents fine anatomical structures,which is important for improving the efficiency of expert diagnosis and realising smart healthcare.However,due to the cost of scanning equipment and the time required for scanning,obtaining an HR brain MRI is quite challenging.Therefore,to improve the quality of images,reference-based super-resolution technology has come into existence.Nevertheless,the existing methods still have some drawbacks:(1)The advantages of different contrast images are not fully utilised.(2)The slice-by-slice scanning nature of magnetic resonance imaging is not considered.(3)The ability to capture contextual information and to match and fuse multi-scale,multi-contrast features is lacking.In this paper,we propose the multi-slice aware matching and fusion(MSAMF)network,which makes full use of multi-slice reference images information by introducing a multi-slice aware module and multi-scale matching strategy to capture corresponding contextual information in reference features at other scales.To further integrate matching features,a multi-scale fusion mechanism is also designed to progressively fuse multi-scale matching features,thereby generating more detailed super-resolution images.The experimental results support the benefits of our network in enhancing the quality of brain MRI reconstruction.展开更多
基金This work was funded by National Natural Science Foundation of China-（No. 40474044）.
文摘In this paper, it is described that the time-frequency resolution of geophysical signals is affected by the time window function attenuation coefficient and sampling interval and how such effects are eliminated effectively. Improving the signal resolution is the key to signal time-frequency analysis processing and has wide use in geophysical data processing and extraction of attribute parameters. In this paper, authors research the effects of the attenuation coefficient choice of the Gabor transform window function and sampling interval on signal resolution. Unsuitable parameters not only decrease the signal resolution on the frequency spectrum but also miss the signals. It is essential to first give the optimum window and range of parameters through time-frequency analysis simulation using the Gabor transform. In the paper, the suggestions about the range and choice of the optimum sampling interval and processing methods of general seismic signals are given.
基金supported by the Innovative Human Resource Development for Local Intel-lectualization program through the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.IITP-2026-2020-0-01741)the research fund of Hanyang University(HY-2025-1110).
文摘Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.
文摘Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].
基金supported by the National Natural Science Foundation of China(Grant Nos.52379098,52274075)the Project of Xingliao Talents Program(XLYC2203008)the Science and Technology Program Project of Liaoning Province(2025JH2/101900011).
文摘Understanding how rock slopes respond to blasting loads is crucial for maintaining excavation safety and slope stability.Nevertheless,the spatiotemporal evolution,nonlinear dependence on blasting parameters,and predictive behavior of dominant frequency responses in slope vibrations remain insufficiently understood and quantified.This study combines time-frequency analysis with machine learning to explore how the dominant frequency(f_(d))evolves in slopes under blasting.Continuous Wavelet Transform(CWT)was employed to characterize the temporal-frequency evolution of vibration signals,revealing that the dominant frequency exhibits strong spatial dependence and nonlinear variability influenced by blasting parameters and rock mass structures.Three machine learning models,namely Back Propagation Neural Network(BP),Support Vector Machine(SVM),and Random Forest(RF),were developed to predict f_(d) based on 1,000 monitoring samples obtained from numerical and field simulations.Among them,the RF model achieved the highest prediction accuracy,with mean absolute percentage errors(MAPE)below 15%,demonstrating strong robustness and generalization capability.Our analysis shows that external excitation factors,especially the loading frequency(f_(d)),mainly control the frequency response,while internal controlling factors,such as spatial position,lithological variation,and mechanical heterogeneity,modulate localized frequency amplification and energy redistribution.The results reveal that f_(d) tends to decrease with elevation and distance from the blasting source,whereas structural planes and weathered zones induce high-frequency amplification due to scattering and modal coupling effects.This study offers a new framework combining time-frequency analysis and machine learning to measure the nonlinear interaction between blasting and rock mass response,offering new insights for dynamic stability evaluation and hazard mitigation in complex rock slope systems.
基金National Natural Science Foundation of China(Grant Nos.42388102,42030105,42192535)the Open Fund of State Key Laboratory of Precision Geodesy,Innovation Academy for Precision Measurement Science and Technology,Chinese Academy of Sciences(Grant No.SKLPG2025-1-5)。
文摘The state-of-the-art optical atomic clocks and the time-frequency signal transmission open a fresh field for gravity potential(geopotential)determination.Various methods,including optical fiber frequency transfer,satellite two-way,satellite common-view,satellite carrier phase,VLBI,tri-frequency combination,and dual-frequency combination,were developed to determine the geopotential differences using optical atomic clocks and then determine the geopotential at station B based on the geopotential at station A.This review elaborates the principles,methods,scientific objectives,applications,and relevant research trends of geopotential determination based on time-frequency signals.
基金supported by the National Natural Science Foundation of China (Grant Nos. 62405231, 62405235, and 62575229)the National Key Laboratory of Space Target Awareness (Grant Nos. STA2024KGL0203, STA2024ZCA0203, and STA-24-04-05)+3 种基金the Beijing Key Laboratory of Advanced Optical Remote Sensing Technology (Grant No. AORS202405)the China Postdoctoral Science Foundation (Grant No. 2024M762527)the Shaanxi Province High-level Innovation and Entrepreneurship Talent Program (Grant No. H02439005)the Natural Science Foundation of Shaanxi (Grant Nos. S2024-JC-JCQN-60, S2025-JCQYTS-0107, and 2025JC-QYCX-05)。
文摘In complex media scattering,multiple scattering severely degrades the optical wavefront and results in blurred images,while the spectral distortion caused by the scattering effect leads to severe color distortion.Achieving color high-resolution imaging through scattering media remains a significant challenge.Here,we propose a broadband,polarization-based method for color high-resolution imaging through scattering media.This approach enables high-resolution reconstruction by effectively separating the speckle illumination pattern from the mixed-scattering field information,leveraging polarization common-mode characteristics.Concurrently,it incorporates chromatic balance compensation to correct spectral aliasing in the scattered light field,enabling color high-resolution imaging through complex scattering media.To further optimize color distortion caused by scattering,a compensation strategy combining color constancy and white balance theory is adopted.Experimental results demonstrate that the proposed method significantly enhances both spatial resolution and color fidelity across various scattering conditions and target materials,showcasing strong adaptability and robustness.This approach provides an effective solution for achieving high-resolution color optical imaging in complex scattering environments.
基金supported by the National Natural Science Foundation of China(Nos.22479092 and 22078190)。
文摘The pseudo-two-dimensional(P2D)model plays an important role in exploring physicochemical mechanisms,predicting the state of health,and improving the fast charge capability for Li-ion batteries(LIBs).However,the fast charge leads to the lithium concentration gradient in the solid and electrolyte phases and the non-uniform electrochemical reaction at the solid/electrolyte interface.In order to decouple charge transfer reactions in LIBs under dynamic conditions,understanding the spatio-temporal resolution of the P2D model is urgently required.Till now,the study of this aspect is still insufficient.This work studies the spatio-temporal resolution for dynamic/static electrochemical impedance spectroscopy(DEIS/SEIS)on multiple scales.In detail,DEIS and SEIS with spatio-temporal resolutions are used to decouple charge transfer reactions in LIBs based on the numerical solution of the P2D model in the frequency domain.The calculated results indicate that decoupling solid diffusion requires a high spatial resolution along the r-direction in particles,decoupling electrolyte diffusion and interfacial transfer reaction requires a high spatial resolution along the x-direction,and decoupling charge transfer reactions in LIBs at an extremely low state of charge(SOC)requires an extremely high temporal resolution along the t-direction.Finally,the optimal range of spatio-temporal resolutions for DEIS/SEIS is derived,and the method to decouple charge transfer reactions with spatio-temporal resolutions is developed.
基金The National Natural Science Foundation of China(No.82130027,82301020,82100966)Young Elite Scientists Sponsorship Program by CAST(2024QNRC001)+5 种基金The China Postdoctoral Science Foundation(2023M732283)The National Key Research and Development Program of China(No.2023YFC2413600)The Shanghai Sailing Program(23YF1422000,21YF1424400)Innovative Research Team of High-level Local Universities in Shanghai(SHSMU-ZLCX20212400)Young Elite Scientists Sponsorship Program by CAST(2021QNRC001)Shanghai Pujiang Program(24PJD054).
文摘Nociceptive pain is a cardinal feature of traumatic and inflammatory bone diseases.However,whether and how nociceptors actively regulate the immune response during bone regeneration remains unclear.Here,we found that neutrophil-triggered nociceptive ingrowth functioned as negative feedback regulation to inflammation during bone healing.A unique Il4ra^(+)Ccl2^(high) neutrophil subset drove intense postinjury TRPV1^(+)nociceptive ingrowth,which in return dissipated inflammation by activating the production of pro-resolving mediator lipoxin A4(LXA4)in osteoblasts.Mechanistically,osteoblastic autophagy activated by nociceptor-derived calcitonin gene-related peptide(CGRP)suppressed the nuclear translocation of arachidonate 5-lipoxygenase(5-LOX)to favor the LXA4 biosynthesis.Moreover,in alveolar bone from patients with Type Ⅱ diabetes,we found diminished nociceptive innervation correlated with reduced autophagy,increased inflammation,and impaired bone formation.Activating nociceptive nerves by spicy diet or topical administration of a clinical-approved TRPV1 agonist showed therapeutic benefits on alveolar bone healing in diabetic mice.These results reveal a critical neuroimmune interaction underlying the inflammation-regeneration balance during bone repairing and may lead to novel therapeutic strategies for inflammatory bone diseases.
基金supported by the National Natural Science Foundation of China(12003068)the Yunnan Key Laboratory of Solar Physics and Space Science(202205AG070009).
文摘High-resolution solar observations are critical for resolving small-scale dynamic solar processes.Specifically,solar continuum observations,which are used to characterize the photospheric radiative energy distribution,identify atmospheric temperature gradients,and model space weather events,serve as a cornerstone of solar physics research.However,existing observational frameworks face inherent limitations:space-based instruments are constrained by diffraction limits,while ground-based data suffer from atmospheric turbulence and temporal discontinuity.To address these challenges,this study proposes a resolution enhancement method based on cross-platform data fusion between Solar Dynamics Observatory(SDO)/Helioseismic and Magnetic Imager(HMI)space-based full-disk coverage observations and Optical and Near-infrared Solar Eruption Telescope(ONSET)ground-based high-resolution local observations to overcome the physical limitations faced by single-instrument observations.Using 6537 preprocessed spatiotemporally aligned datasets(from 2022),we achieve sub-pixel registration via the scale-invariant feature transform(SIFT)algorithm and design a lightweight model called Cross-Instrument Super-Resolution(CISR)based on a residual local feature block network,optimized for feature extraction and reconstruction using the smooth L1-loss function.Experimental results demonstrate that CISR achieves a pixel-wise correlation coefficient of 0.946,a peak signal-to-noise ratio(PSNR)of 33.924 dB,and a structural similarity index of 0.855 on the test set,significantly outperforming bicubic interpolation and the Super-Resolution Convolutional Neural Network(SRCNN)baseline model.Qualitative visual assessment verifies the method’s efficacy for HMI continuum data resolution enhancement,with exceptional performance in maintaining both sunspot boundary acuity and granule structural fidelity.This work provides a novel approach for multi-source solar data synergy,with future potential to incorporate physics-driven evaluation metrics to further improve the model generalization.
基金supported in part by the National Natural Science Foundation of China(62372385,62272078,62002337)Chongqing Natural Science Foundation(CSTB2022NSCQ-MSX1486,CSTB2023NSCQ-LZX0069)。
文摘A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spatial-temporal message passing mechanism built on tensor product.Concretely,an HGCN utilizes the discrete Fourier transform(DFT)to implement temporal message passing and then employs face-wise product to realize spatial message passing.However,DFT is only a special case of assorted time-frequency transforms,which considers the complex temporal patterns partially,thereby resulting in an inaccurate temporal message passing possibly.To address this issue,this study proposes six advanced time-frequency transform-incorporated HGCNs(TF-HGCNs)with discrete Fourier,discrete Hartley,discrete cosine,Haar wavelet,Walsh Hadamard,and slant transforms.In addition,a potent ensemble is built regarding the proposed six TF-HGCNs as the bases.Finally,the corresponding theoretical proof is presented.Empirical studies on six DG datasets demonstrate that owing to diverse time-frequency transforms,the proposed six TF-HGCNs significantly outperform state-of-the-art models in addressing the task of link weight estimation.Moreover,their ensemble outstrips each base's performance.
基金Supported by the China National Science and Technology Major Project(2025ZD1407000)PetroChina Science and Technology Major Project(2023ZZ22)。
文摘To address the challenges of complex fluvial sandbody distribution and difficult remaining oil recovery in mature continental oilfields,this study focuses on key issues in reservoir identification such as ambiguous narrow-channel boundaries and subdivision of multi-stage superimposed sandbodies.Taking the Upper Cretaceous continental sandstone in the Sazhong Oilfield of the Daqing Placanticline as an example,a technical system integrating OVT high-resolution processing,multi-attribute fusion,and varible-scale inversion was developed to establish a complete workflow from seismic processing to reservoir prediction and remaining oil recovery.The following results are obtained.First,the Offset Vector Tile(OVT)seismic processing technology is extended,for the first time,from fracture imaging to sandbody prediction,in order to address the weak seismic responses from boundaries of narrow and thin sandbodies.A geology-oriented OVT partitioning method is developed to significantly improve the imaging accuracy,enabling identification of channel sandbodies as narrow as 50 m.Second,an amplitude-coherence dual-attribute fusion method is proposed for predicting narrow channel boundaries between wells.Constrained by a sedimentary unit-level sequence chronostratigraphic framework,this method accurately delineates 800-2000 m long subaqueous distributary channels with bifurcation-convergence features.Third,considering the superimposition of multi-stage channels,a three-level variable-scale stratigraphic model(sandstone groups,sublayers,sedimentary units)is constructed to overcome single-scale modeling limitations,successfully characterizing key sedimentary features like meandering river“cut-offs”through 3D seismic inversion.Based on these advances,a direct link between seismic prediction and remaining oil recovery is established.The horizontal wells deployed using narrow-channel predictions encountered oil-bearing sandstones in the horizontal section by 97%,and achieved initial daily production of 12.5 t per well.Precise identification of individual channel boundaries within 17 composite sandbodies guided recovery processes in 135 wells,yielding an average daily increase of 2.8 t per well and a cumulative increase of 13.6×10^(4)t.
基金financially supported by the National 973 Project(No.2014CB239006)the National Natural Science Foundation of China(No.41104069 and 41274124)the Fundamental Research Funds for Central Universities(No.R1401005A)
文摘The resolution of seismic data is critical to seismic data processing and the subsequent interpretation of fine structures. In conventional resolution improvement methods, the seismic data is assumed stationary and the noise level not changes with space, whereas the actual situation does not satisfy this assumption, so that results after resolution improvement processing is not up to the expected effect. To solve these problems, we propose a seismic resolution improvement method based on the secondary time-frequency spectrum. First, we propose the secondary time-frequency spectrum based on S transform （ST） and discuss the reflection coefficient sequence and time-dependent wavelet in the secondary time frequency spectrum. Second, using the secondary time frequency spectrum, we design a two- dimensional filter to extract the amplitude spectrum of the time-dependent wavelet. Then, we discuss the improvement of the resolution operator in noisy environments and propose a novel approach for determining the broad frequency range of the resolution operator in the time- fi＇equency-space domain. Finally, we apply the proposed method to synthetic and real data and compare the results of the traditional spectrum-modeling deconvolution and Q compensation method. The results suggest that the proposed method does not need to estimate the Q value and the resolution is not limited by the bandwidth of the source. Thus, the resolution of the seismic data is improved sufficiently based on the signal-to-noise ratio （SNR）.
基金financial supports from National Natural Science Foundation of China(Grant Nos.U23A20368 and 62175006)Academic Excellence Foundation of BUAA for PhD Students.
文摘Due to the limitations of spatial bandwidth product and data transmission bandwidth,the field of view,resolution,and imaging speed constrain each other in an optical imaging system.Here,a fast-zoom and high-resolution sparse compound-eye camera(CEC)based on dual-end collaborative optimization is proposed,which provides a cost-effective way to break through the trade-off among the field of view,resolution,and imaging speed.In the optical end,a sparse CEC based on liquid lenses is designed,which can realize large-field-of-view imaging in real time,and fast zooming within 5 ms.In the computational end,a disturbed degradation model driven super-resolution network(DDMDSR-Net)is proposed to deal with complex image degradation issues in actual imaging situations,achieving high-robustness and high-fidelity resolution enhancement.Based on the proposed dual-end collaborative optimization framework,the angular resolution of the CEC can be enhanced from 71.6"to 26.0",which provides a solution to realize high-resolution imaging for array camera dispensing with high optical hardware complexity and data transmission bandwidth.Experiments verify the advantages of the CEC based on dual-end collaborative optimization in high-fidelity reconstruction of real scene images,kilometer-level long-distance detection,and dynamic imaging and precise recognition of targets of interest.
基金supported by the National Key R&D Program of China(2022YFA1602200)the International Partnership Program of the Chinese Academy of Sciences(211134KYSB20200057).
文摘The study of the charge conjugation and parity(CP)violation of hyperon is the precision frontier for probing possible new CP violation sources beyond the standard model(SM).With the large number of quantum entangled hyperonantihyperon pairs to be produced at Super Tau-Charm Facility(STCF),the CP asymmetry of hyperon is expected to be tested with a statistical sensitivity of 10^(−4) or even better.To cope with the statistical precision,the systematic effects from various aspects are critical and need to be studied in detail.In this paper,the sensitivity effects on the CP violation parameters associated with the detector resolution,including those of the position and momentum,are studied and discussed in detail.The results provide valuable guidance for the design of STCF detector.
基金supported by the National Natural Science Foundation of China(Grant Nos.42250103,42174090,42250101,42250102,and 41774091)the Macao Foundation+1 种基金the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB2023ZR02)the MOST Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources(Grant No.MSFGPMR2022-4)。
文摘The CUG_CLMFM3D series comprises high-resolution three-dimensional lithospheric magnetic field models for China and its surroundings.The first version,CUG_CLMFM3Dv1,is a spherical cap harmonic model integrating the WDMAMv2(World Digital Magnetic Anomaly Map version 2)global magnetic anomaly grid and nearly a decade of CHAMP(Challenging Minisatellite Payload for Geophysical Research and Application)satellite vector data.It achieves a~5.7 km resolution but has limitations:the WDMAMv2 grid lacks high-resolution data in the southern Xinjiang and Tibet regions,which leads to missing small-to medium-scale anomalies,and unfiltered CHAMP data introduce low-frequency conflicts with global spherical harmonic models.Above the altitude of 150 km,correlations with global models drop below 0.9.The second version,CUG_CLMFM3Dv2,addresses these issues by incorporating 5-km-resolution aeromagnetic data and rigorously processed satellite data from CHAMP,Swarm,CSES-1(China Seismo-Electromagnetic Satellite 1),and MSS-1(Macao Science Satellite 1).The comparison analysis shows that the CUG_CLMFM3Dv2 captures finer high-frequency details and more stable long-wavelength signals,offering improved magnetic anomaly maps for further geological and geophysical studies.
基金Support by the Fundamental Research Funds for the Central Universities(2024300443)the National Natural Science Foundation of China(NSFC)Young Scientists Fund(62405131)。
文摘This article proposes a three-dimensional light field reconstruction method based on neural radiation field(NeRF)called Infrared NeRF for low resolution thermal infrared scenes.Based on the characteristics of the low resolution thermal infrared imaging,various optimizations have been carried out to improve the speed and accuracy of thermal infrared 3D reconstruction.Firstly,inspired by Boltzmann's law of thermal radiation,distance is incorporated into the NeRF model for the first time,resulting in a nonlinear propagation of a single ray and a more accurate description of the physical property that infrared radiation intensity decreases with increasing distance.Secondly,in terms of improving inference speed,based on the phenomenon of high and low frequency distribution of foreground and background in infrared images,a multi ray non-uniform light synthesis strategy is proposed to make the model pay more attention to foreground objects in the scene,reduce the distribution of light in the background,and significantly reduce training time without reducing accuracy.In addition,compared to visible light scenes,infrared images only have a single channel,so fewer network parameters are required.Experiments using the same training data and data filtering method showed that,compared to the original NeRF,the improved network achieved an average improvement of 13.8%and 4.62%in PSNR and SSIM,respectively,while an average decreases of 46%in LPIPS.And thanks to the optimization of network layers and data filtering methods,training only takes about 25%of the original method's time to achieve convergence.Finally,for scenes with weak backgrounds,this article improves the inference speed of the model by 4-6 times compared to the original NeRF by limiting the query interval of the model.
基金supported by the German Research Foundation(DFG)(Grant Nos.LE 992/14-3 and LE 992/15-3).
文摘Microsphere and microcylinder-assisted microscopy(MAM)has grown steadily over the last decade and is still an intensively studied optical far-field imaging technique that promises to overcome the fundamental lateral resolution limit of microscopy.However,the physical effects leading to resolution enhancement are still frequently debated.In addition,various configurations of MAM operating in transmission mode as well as reflection mode are examined,and the results are sometimes generalized.We present a rigorous simulation model of MAM and introduce a way to quantify the resolution enhancement.The lateral resolution is compared for microscope arrangements in reflection and transmission modes.Furthermore,we discuss different physical effects with respect to their contribution to resolution enhancement.The results indicate that the effects impacting the resolution in MAM strongly depend on the arrangement of the microscope and the measurement object.As a highlight,we outline that evanescent waves in combination with whispering gallery modes also improve the imaging capabilities,enabling super-resolution under certain circumstances.This result is contrary to the conclusions drawn from previous studies,where phase objects have been analyzed,and thus further emphasizes the complexity of the physical mechanisms underlying MAM.
文摘In February 2025,a startup satellite manufacturer,Albedo(Broomfield,CO,USA)is expected to launch its first satellite,Clarity-1,into orbit aboard SpaceX’s Transporter-13,a Falcon 9 rideshare mission[1].Like many imaging satellites,Clarity-1’s mis-sion will be to take high-resolution aerial photos for clients in var-ious economic sectors including agriculture,insurance,energy,mapping,utilities,and defense.What makes this satellite unique is both its industry-leading 10 cm spatial resolution and its extre-mely low orbit of 200 km,far closer to Earth than the 450 km or higher orbits of most of its peers with similar missions.
基金supported by Natural Science Foundation of China(No.62371231)Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu under Grant BK20222001Jiangsu Provincial Key Research and Development Program(No.BE2023027).
文摘With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communication system.In particular,the abnormal signals may emulate the normal signals,which makes it very challenging for abnormal signal recognition.In this paper,we propose a new abnormal signal recognition scheme,which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals.Firstly,we emulate synthetic abnormal communication signals including seven jamming patterns.Then,we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver.To improve the performance,we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm.Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations,even under low signal-to-noise ratio(SNR)and low jamming-to-signal ratio(JSR)conditions.
基金supported by the National Natural Science Foundation of China(Grants 62376184,62206196,62403345,62303445)Shanxi Provincial Special Guidance Program for the Transformation of Scientific and Technological Achievements(Grants 202304021301035,202404021301032)Central Guided Local Science and Technology Development Project(Grant YDZJSX20231A017).
文摘In clinical diagnosis,magnetic resonance imaging(MRI)allows different contrast images to be obtained.High-resolution(HR)MRI presents fine anatomical structures,which is important for improving the efficiency of expert diagnosis and realising smart healthcare.However,due to the cost of scanning equipment and the time required for scanning,obtaining an HR brain MRI is quite challenging.Therefore,to improve the quality of images,reference-based super-resolution technology has come into existence.Nevertheless,the existing methods still have some drawbacks:(1)The advantages of different contrast images are not fully utilised.(2)The slice-by-slice scanning nature of magnetic resonance imaging is not considered.(3)The ability to capture contextual information and to match and fuse multi-scale,multi-contrast features is lacking.In this paper,we propose the multi-slice aware matching and fusion(MSAMF)network,which makes full use of multi-slice reference images information by introducing a multi-slice aware module and multi-scale matching strategy to capture corresponding contextual information in reference features at other scales.To further integrate matching features,a multi-scale fusion mechanism is also designed to progressively fuse multi-scale matching features,thereby generating more detailed super-resolution images.The experimental results support the benefits of our network in enhancing the quality of brain MRI reconstruction.
