Generating realistic and synthetic video from text is a highly challenging task due to the multitude of issues involved,including digit deformation,noise interference between frames,blurred output,and the need for tem...Generating realistic and synthetic video from text is a highly challenging task due to the multitude of issues involved,including digit deformation,noise interference between frames,blurred output,and the need for temporal coherence across frames.In this paper,we propose a novel approach for generating coherent videos of moving digits from textual input using a Deep Deconvolutional Generative Adversarial Network(DD-GAN).The DDGAN comprises a Deep Deconvolutional Neural Network(DDNN)as a Generator(G)and a modified Deep Convolutional Neural Network(DCNN)as a Discriminator(D)to ensure temporal coherence between adjacent frames.The proposed research involves several steps.First,the input text is fed into a Long Short Term Memory(LSTM)based text encoder and then smoothed using Conditioning Augmentation(CA)techniques to enhance the effectiveness of the Generator(G).Next,using a DDNN to generate video frames by incorporating enhanced text and random noise and modifying a DCNN to act as a Discriminator(D),effectively distinguishing between generated and real videos.This research evaluates the quality of the generated videos using standard metrics like Inception Score(IS),Fréchet Inception Distance(FID),Fréchet Inception Distance for video(FID2vid),and Generative Adversarial Metric(GAM),along with a human study based on realism,coherence,and relevance.By conducting experiments on Single-Digit Bouncing MNIST GIFs(SBMG),Two-Digit Bouncing MNIST GIFs(TBMG),and a custom dataset of essential mathematics videos with related text,this research demonstrates significant improvements in both metrics and human study results,confirming the effectiveness of DD-GAN.This research also took the exciting challenge of generating preschool math videos from text,handling complex structures,digits,and symbols,and achieving successful results.The proposed research demonstrates promising results for generating coherent videos from textual input.展开更多
In recent years,the number of patientswith colon disease has increased significantly.Colon polyps are the precursor lesions of colon cancer.If not diagnosed in time,they can easily develop into colon cancer,posing a s...In recent years,the number of patientswith colon disease has increased significantly.Colon polyps are the precursor lesions of colon cancer.If not diagnosed in time,they can easily develop into colon cancer,posing a serious threat to patients’lives and health.A colonoscopy is an important means of detecting colon polyps.However,in polyp imaging,due to the large differences and diverse types of polyps in size,shape,color,etc.,traditional detection methods face the problem of high false positive rates,which creates problems for doctors during the diagnosis process.In order to improve the accuracy and efficiency of colon polyp detection,this question proposes a network model suitable for colon polyp detection(PD-YOLO).This method introduces the self-attention mechanism CBAM(Convolutional Block Attention Module)in the backbone layer based on YOLOv7,allowing themodel to adaptively focus on key information and ignore the unimportant parts.To help themodel do a better job of polyp localization and bounding box regression,add the SPD-Conv(Symmetric Positive Definite Convolution)module to the neck layer and use deconvolution instead of upsampling.Theexperimental results indicate that the PD-YOLO algorithm demonstrates strong robustness in colon polyp detection.Compared to the original YOLOv7,on the Kvasir-SEG dataset,PD-YOLO has shown an increase of 5.44 percentage points in AP@0.5,showcasing significant advantages over other mainstream methods.展开更多
Deconvolution methods are commonly used to improve the performance of phased array beamforming for sound source localization. However, for coherent sources localization, existing deconvolution methods are either highl...Deconvolution methods are commonly used to improve the performance of phased array beamforming for sound source localization. However, for coherent sources localization, existing deconvolution methods are either highly computationally demanding or sensitive to parameters.A deconvolution method, based on modifications of Clean based on Source Coherence(CLEAN-SC), is proposed for coherent sources localization. This method is called Coherence CLEAN-SC(C–CLEAN-SC). C–CLEAN-SC is able to locate coherent and incoherent sources in simulation and experimental cases. It has a high computational efficiency and does not require pre-set parameters.展开更多
KanCell is a deep learning model based on Kolmogorov-Arnold networks(KAN)designed to enhance cellular heterogeneity analysis by integrating single-cell RNA sequencing and spatial transcriptomics(ST)data.ST technologie...KanCell is a deep learning model based on Kolmogorov-Arnold networks(KAN)designed to enhance cellular heterogeneity analysis by integrating single-cell RNA sequencing and spatial transcriptomics(ST)data.ST technologies provide insights into gene expression within tissue context,revealing cellular interactions and microenvironments.To fully leverage this potential,effective computational models are crucial.We evaluate KanCell on both simulated and real datasets from technologies such as STARmap,Slide-seq,Visium,and Spatial Transcriptomics.Our results demonstrate that KanCell outperforms existing methods across metrics like PCC,SSIM,COSSIM,RMSE,JSD,ARS,and ROC,with robust performance under varying cell numbers and background noise.Real-world applications on human lymph nodes,hearts,melanoma,breast cancer,dorsolateral prefrontal cortex,and mouse embryo brains confirmed its reliability.Compared with traditional approaches,KanCell effectively captures non-linear relationships and optimizes computational efficiency through KAN,providing an accurate and efficient tool for ST.By improving data accuracy and resolving cell type composition,KanCell reveals cellular heterogeneity,clarifies disease microenvironments,and identifies therapeutic targets,addressing complex biological challenges.展开更多
The performance of the deconvolution algorithm plays a crucial role in data processing of radio interferometers.The multi-scale multi-frequency synthesis(MSMFS)CLEAN is a widely used deconvolution algorithm for radio ...The performance of the deconvolution algorithm plays a crucial role in data processing of radio interferometers.The multi-scale multi-frequency synthesis(MSMFS)CLEAN is a widely used deconvolution algorithm for radio interferometric imaging,which combines the advantages of both wide-band synthesis imaging and multi-scale imaging and can substantially improve performance.However,how best to effectively determine the optimal scale is an important problem when implementing the MSMFS CLEAN algorithm.In this study,we proposed a Gaussian fitting method for multiple sources based on the gradient descent algorithm,with consideration of the influence of the point spread function(PSF).After fitting,we analyzed the fitting components using statistical analysis to derive reasonable scale information through the model parameters.A series of simulation validations demonstrated that the scales extracted by our proposed algorithm are accurate and reasonable.The proposed method can be applied to the deconvolution algorithm and provide modeling analysis for Gaussian sources,offering data support for source extraction algorithms.展开更多
Optical-resolution photoacoustic microscopy is a novel imaging technique that combines the advantages of optical and ultrasound imaging,enabling high-resolution visualization of biological tissues at the micrometer sc...Optical-resolution photoacoustic microscopy is a novel imaging technique that combines the advantages of optical and ultrasound imaging,enabling high-resolution visualization of biological tissues at the micrometer scale.However,the divergence of the excited Gaussian beam limits the depth-of-field of the system to less than 100μm,which hinders accurate three-dimensional imaging of living tissues and restrictsits applicability in biological research.Therefore,there is an urgent need for an effective method to enhance the depth-of-field without altering the hardware configuration.This paper presents a photoacoustic microscopy depth-of-field extension method and system based on three-dimensional continuity and sparsity deconvolution.This method utilizes a depth-varying point spread function and incorporates continuity and sparsity con-straints into the deconvolution process to mitigate the effect of background noise,enhancing the stability and accuracy of the depth-of-field extension.Experimental results using tungsten wire phantoms suggest that the depth-of-field of system can be extended to 650 pm,which is 7.2 times greater than conventional system,while improving the resolution of the defocused region by an average factor of 3.5.Furthermore,experiments on zebrafish and nude mouse ears with irregular topologies demonstrate that the proposed method successfully overcomes image blurring and the loss of structural information due to limited depth-of-field.All the results suggest that the system with higher lateral resolution and enhanced depth-of-field has significant potential for a wide range of practical biomedical applications.展开更多
This work focuses on enhancing low frequency seismic data using a convolutional neural network trained on synthetic data.Traditional seismic data often lack both high and low frequencies,which are essential for detail...This work focuses on enhancing low frequency seismic data using a convolutional neural network trained on synthetic data.Traditional seismic data often lack both high and low frequencies,which are essential for detailed geological interpretation and various geophysical applications.Low frequency data is particularly valuable for reducing wavelet sidelobes and improving full waveform inversion(FWI).Conventional methods for bandwidth extension include seismic deconvolution and sparse inversion,which have limitations in recovering low frequencies.The study explores the potential of the U-net,which has been successful in other geophysical applications such as noise attenuation and seismic resolution enhancement.The novelty in our approach is that we do not rely on computationally expensive finite difference modelling to create training data.Instead,our synthetic training data is created from individual randomly perturbed events with variations in bandwidth,making it more adaptable to different data sets compared to previous deep learning methods.The method was tested on both synthetic and real seismic data,demonstrating effective low frequency reconstruction and sidelobe reduction.With a synthetic full waveform inversion to recover a velocity model and a seismic amplitude inversion to estimate acoustic impedance we demonstrate the validity and benefit of the proposed method.Overall,the study presents a robust approach to seismic bandwidth extension using deep learning,emphasizing the importance of diverse and well-designed but computationally inexpensive synthetic training data.展开更多
Traditional deconvolution methods based on single-channel inversion do not consider the spatial structural relation between channels,and hence,they yield high-resolution results with the existing transverse inconsiste...Traditional deconvolution methods based on single-channel inversion do not consider the spatial structural relation between channels,and hence,they yield high-resolution results with the existing transverse inconsistency or discontinuity.Therefore,in this study,the local dip angle was used to obtain the structural information and construct the spatial structurally constraint operator.This operator is then introduced into multichannel deconvolution as a regularization operator to improve the resolution and maintain the transverse continuity of seismic data.Model tests and actual seismic data processing have demonstrated the effectiveness and practicability of this method.展开更多
Double perovskite matrix materials have recently attracted considerable interest due to their structural flexibility,ease of doping,and excellent thermal stability.While photoluminescence(PL)studies of rare-earth-dope...Double perovskite matrix materials have recently attracted considerable interest due to their structural flexibility,ease of doping,and excellent thermal stability.While photoluminescence(PL)studies of rare-earth-doped double perovskites are common,research on their thermoluminescence(TL)properties is less extensive.This study synthesized a series of Y_(2-x)Sm_(x)MgTiO_(6)(0≤x≤0.1)samples using a high-temperature solid-state method.X-ray diffraction(XRD)analysis confirmed a monoclinic crystal structure(space group P2_(1)∕n),with Sm^(3+)ions substituting for Y^(3+)ions in Y_(2)MgTiO_(6).The PL results indicated that the optimal doping concentration was Y_(1.95)Sm_(0.05)MgTiO_(6),exhibiting emission peaks at 568,605,652,and 715 nm under 409 nm blue light excitation.The TL measurements for different doping concentrations showed that the Y_(1.98)Sm_(0.02)MgTiO_(6)phosphors exhibited the strongest TL signals.The TL peaks observed at 530 and 610 K correspond to defects in the matrix and Sm^(3+)dopants,respectively.The T_(m)-T_(stop)analysis revealed that the TL curve of Y_(1.98)Sm_(0.02)MgTiO_(6)phosphors was a superposition of seven peaks.Computerized glow curve deconvolution(CGCD)was performed on the TL of the sample according to the results of three-dimensional thermoluminescence spectra(3D-TL)and T_(m)-T_(stop),and the trap depths in the sample were estimated to range from 0.69 to 1.49 eV.Additionally,the lifetimes of each overlapping peak were calculated using the fitting parameters.Furthermore,the dose-response test showed that the saturation dose of the sample was high(9956 Gy).Therefore,this material can serve as a thermoluminescent dosimeter for high-dose measurements.The saturation dose for the lowest-temperature overlapping peak was 102 Gy,which correlated with its specific energy-level lifetime,whereas the other overlapping peaks also exhibited favorable linear relationships.展开更多
The conventional nonstationary convolutional model assumes that the seismic signal is recorded at normal incidence. Raw shot gathers are far from this assumption because of the effects of offsets. Because of such prob...The conventional nonstationary convolutional model assumes that the seismic signal is recorded at normal incidence. Raw shot gathers are far from this assumption because of the effects of offsets. Because of such problems, we propose a novel prestack nonstationary deconvolution approach. We introduce the radial trace （RT） transform to the nonstationary deconvolution, we estimate the nonstationary deconvolution factor with hyperbolic smoothing based on variable-step sampling （VSS） in the RT domain, and we obtain the high-resolution prestack nonstationary deconvolution data. The RT transform maps the shot record from the offset and traveltime coordinates to those of apparent velocity and traveltime. The ray paths of the traces in the RT better satisfy the assumptions of the convolutional model. The proposed method combines the advantages of stationary deconvolution and inverse Q filtering, without prior information for Q. The nonstationary deconvolution in the RT domain is more suitable than that in the space-time （XT） domain for prestack data because it is the generalized extension of normal incidence. Tests with synthetic and real data demonstrate that the proposed method is more effective in compensating for large-offset and deep data.展开更多
The Robinson convolution model is mainly restricted by three inappropriate assumptions, i.e., statistically white reflectivity, minimum-phase wavelet, and stationarity. Modern reflectivity inversion methods（e.g., spa...The Robinson convolution model is mainly restricted by three inappropriate assumptions, i.e., statistically white reflectivity, minimum-phase wavelet, and stationarity. Modern reflectivity inversion methods（e.g., sparsity-constrained deconvolution） generally attempt to suppress the problems associated with the first two assumptions but often ignore that seismic traces are nonstationary signals, which undermines the basic assumption of unchanging wavelet in reflectivity inversion. Through tests on reflectivity series, we confirm the effects of nonstationarity on reflectivity estimation and the loss of significant information, especially in deep layers. To overcome the problems caused by nonstationarity, we propose a nonstationary convolutional model, and then use the attenuation curve in log spectra to detect and correct the influences of nonstationarity. We use Gabor deconvolution to handle nonstationarity and sparsity-constrained deconvolution to separating reflectivity and wavelet. The combination of the two deconvolution methods effectively handles nonstationarity and greatly reduces the problems associated with the unreasonable assumptions regarding reflectivity and wavelet. Using marine seismic data, we show that correcting nonstationarity helps recover subtle reflectivity information and enhances the characterization of details with respect to the geological record.展开更多
We propose a new automatic method for the interpretation of potential fi eld data, called the RDAS–Euler method, which is based on Euler＇s deconvolution and analytic signal methods. The proposed method can estimate ...We propose a new automatic method for the interpretation of potential fi eld data, called the RDAS–Euler method, which is based on Euler＇s deconvolution and analytic signal methods. The proposed method can estimate the horizontal and vertical extent of geophysical anomalies without prior information of the nature of the anomalies（structural index）. It also avoids inversion errors because of the erroneous choice of the structural index N in the conventional Euler deconvolution method. The method was tested using model gravity anomalies. In all cases, the misfi t between theoretical values and inversion results is less than 10%. Relative to the conventional Euler deconvolution method, the RDAS–Euler method produces inversion results that are more stable and accurate. Finally, we demonstrate the practicability of the method by applying it to Hulin Basin in Heilongjiang province, where the proposed method produced more accurate data regarding the distribution of faults.展开更多
In seismic data processing, blind deconvolution is a key technology. Introduced in this paper is a flow of one kind of blind deconvolution. The optimal precondition conjugate gradients (PCG) in Kyrlov subspace is als...In seismic data processing, blind deconvolution is a key technology. Introduced in this paper is a flow of one kind of blind deconvolution. The optimal precondition conjugate gradients (PCG) in Kyrlov subspace is also used to improve the stability of the algorithm. The computation amount is greatly decreased.展开更多
In order to suppress the airwave noise in marine controlled-source electromagnetic （CSEM） data, we propose a 3D deconvolution （3DD） interferometry method with a synthetic aperture source and obtain the relative an...In order to suppress the airwave noise in marine controlled-source electromagnetic （CSEM） data, we propose a 3D deconvolution （3DD） interferometry method with a synthetic aperture source and obtain the relative anomaly coefficient （RAC） of the EM field reflection responses to show the degree for suppressing the airwave. We analyze the potential of the proposed method for suppressing the airwave, and compare the proposed method with traditional methods in their effectiveness. A method to select synthetic source length is derived and the effect of the water depth on RAC is examined via numerical simulations. The results suggest that 3DD interferometry method with a synthetic source can effectively suppress the airwave and enhance the potential of marine CSEM to hydrocarbon exploration.展开更多
Sparsity constrained deconvolution can improve the resolution of band-limited seismic data compared to conventional deconvolution. However, such deconvolution methods result in nonunique solutions and suppress weak re...Sparsity constrained deconvolution can improve the resolution of band-limited seismic data compared to conventional deconvolution. However, such deconvolution methods result in nonunique solutions and suppress weak reflections. The Cauchy function, modified Cauchy function, and Huber function are commonly used constraint criteria in sparse deconvolution. We used numerical experiments to analyze the ability of sparsity constrained deconvolution to restore reflectivity sequences and protect weak reflections under different constraint criteria. The experimental results demonstrate that the performance of sparsity constrained deconvolution depends on the agreement between the constraint criteria and the probability distribution of the reflectivity sequences; furthermore, the modified Cauchy- constrained criterion protects the weak reflections better than the other criteria. Based on the model experiments, the probability distribution of the reflectivity sequences of carbonate and clastic formations is statistically analyzed by using well-logging data and then the modified Cauchy-constrained deconvolution is applied to real seismic data much improving the resolution.展开更多
The predictive deconvolution algorithm （PD）, which is based on second-order statistics, assumes that the primaries and the multiples are implicitly orthogonal. However, the seismic data usually do not satisfy this a...The predictive deconvolution algorithm （PD）, which is based on second-order statistics, assumes that the primaries and the multiples are implicitly orthogonal. However, the seismic data usually do not satisfy this assumption in practice. Since the seismic data （primaries and multiples） have a non-Gaussian distribution, in this paper we present an improved predictive deconvolution algorithm （IPD） by maximizing the non-Gaussianity of the recovered primaries. Applications of the IPD method on synthetic and real seismic datasets show that the proposed method obtains promising results.展开更多
A novel algorithm, i.e. the fast alternating direction method of multipliers （ADMM）, is applied to solve the classical total-variation （ TV ）-based model for image reconstruction. First, the TV-based model is refo...A novel algorithm, i.e. the fast alternating direction method of multipliers （ADMM）, is applied to solve the classical total-variation （ TV ）-based model for image reconstruction. First, the TV-based model is reformulated as a linear equality constrained problem where the objective function is separable. Then, by introducing the augmented Lagrangian function, the two variables are alternatively minimized by the Gauss-Seidel idea. Finally, the dual variable is updated. Because the approach makes full use of the special structure of the problem and decomposes the original problem into several low-dimensional sub-problems, the per iteration computational complexity of the approach is dominated by two fast Fourier transforms. Elementary experimental results indicate that the proposed approach is more stable and efficient compared with some state-of-the-art algorithms.展开更多
This paper presents a new blind XPIC and a new adaptive blind deconvolutional algorithm based on HOS processing, which separates and equalizes the signals in real time. The simulation results demonstrate that the perf...This paper presents a new blind XPIC and a new adaptive blind deconvolutional algorithm based on HOS processing, which separates and equalizes the signals in real time. The simulation results demonstrate that the performance of the proposed adaptive blind algorithm,compared with the conventional algorithms, is outstanding with the feature of feasibility, stability and fast convergence rate.展开更多
基金supported by the General Program of the National Natural Science Foundation of China(Grant No.61977029).
文摘Generating realistic and synthetic video from text is a highly challenging task due to the multitude of issues involved,including digit deformation,noise interference between frames,blurred output,and the need for temporal coherence across frames.In this paper,we propose a novel approach for generating coherent videos of moving digits from textual input using a Deep Deconvolutional Generative Adversarial Network(DD-GAN).The DDGAN comprises a Deep Deconvolutional Neural Network(DDNN)as a Generator(G)and a modified Deep Convolutional Neural Network(DCNN)as a Discriminator(D)to ensure temporal coherence between adjacent frames.The proposed research involves several steps.First,the input text is fed into a Long Short Term Memory(LSTM)based text encoder and then smoothed using Conditioning Augmentation(CA)techniques to enhance the effectiveness of the Generator(G).Next,using a DDNN to generate video frames by incorporating enhanced text and random noise and modifying a DCNN to act as a Discriminator(D),effectively distinguishing between generated and real videos.This research evaluates the quality of the generated videos using standard metrics like Inception Score(IS),Fréchet Inception Distance(FID),Fréchet Inception Distance for video(FID2vid),and Generative Adversarial Metric(GAM),along with a human study based on realism,coherence,and relevance.By conducting experiments on Single-Digit Bouncing MNIST GIFs(SBMG),Two-Digit Bouncing MNIST GIFs(TBMG),and a custom dataset of essential mathematics videos with related text,this research demonstrates significant improvements in both metrics and human study results,confirming the effectiveness of DD-GAN.This research also took the exciting challenge of generating preschool math videos from text,handling complex structures,digits,and symbols,and achieving successful results.The proposed research demonstrates promising results for generating coherent videos from textual input.
基金funded by the Undergraduate Higher Education Teaching and Research Project(No.FBJY20230216)Research Projects of Putian University(No.2023043)the Education Department of the Fujian Province Project(No.JAT220300).
文摘In recent years,the number of patientswith colon disease has increased significantly.Colon polyps are the precursor lesions of colon cancer.If not diagnosed in time,they can easily develop into colon cancer,posing a serious threat to patients’lives and health.A colonoscopy is an important means of detecting colon polyps.However,in polyp imaging,due to the large differences and diverse types of polyps in size,shape,color,etc.,traditional detection methods face the problem of high false positive rates,which creates problems for doctors during the diagnosis process.In order to improve the accuracy and efficiency of colon polyp detection,this question proposes a network model suitable for colon polyp detection(PD-YOLO).This method introduces the self-attention mechanism CBAM(Convolutional Block Attention Module)in the backbone layer based on YOLOv7,allowing themodel to adaptively focus on key information and ignore the unimportant parts.To help themodel do a better job of polyp localization and bounding box regression,add the SPD-Conv(Symmetric Positive Definite Convolution)module to the neck layer and use deconvolution instead of upsampling.Theexperimental results indicate that the PD-YOLO algorithm demonstrates strong robustness in colon polyp detection.Compared to the original YOLOv7,on the Kvasir-SEG dataset,PD-YOLO has shown an increase of 5.44 percentage points in AP@0.5,showcasing significant advantages over other mainstream methods.
基金supported by the National Science and Technology Major Project of China (No. 2017-II-003–0015)。
文摘Deconvolution methods are commonly used to improve the performance of phased array beamforming for sound source localization. However, for coherent sources localization, existing deconvolution methods are either highly computationally demanding or sensitive to parameters.A deconvolution method, based on modifications of Clean based on Source Coherence(CLEAN-SC), is proposed for coherent sources localization. This method is called Coherence CLEAN-SC(C–CLEAN-SC). C–CLEAN-SC is able to locate coherent and incoherent sources in simulation and experimental cases. It has a high computational efficiency and does not require pre-set parameters.
基金supported by the National Natural Science Foundation of China(52361145714,21673252)the Beijing Municipal Education Commission(2019821001)+1 种基金Climbing Program Foundation from Beijing Institute of Petrochemical Technology(BIPTAAl-2021007)the ZhiYuan Fund key Project from Beijing Institute of Petrochemical Technology(2024003).
文摘KanCell is a deep learning model based on Kolmogorov-Arnold networks(KAN)designed to enhance cellular heterogeneity analysis by integrating single-cell RNA sequencing and spatial transcriptomics(ST)data.ST technologies provide insights into gene expression within tissue context,revealing cellular interactions and microenvironments.To fully leverage this potential,effective computational models are crucial.We evaluate KanCell on both simulated and real datasets from technologies such as STARmap,Slide-seq,Visium,and Spatial Transcriptomics.Our results demonstrate that KanCell outperforms existing methods across metrics like PCC,SSIM,COSSIM,RMSE,JSD,ARS,and ROC,with robust performance under varying cell numbers and background noise.Real-world applications on human lymph nodes,hearts,melanoma,breast cancer,dorsolateral prefrontal cortex,and mouse embryo brains confirmed its reliability.Compared with traditional approaches,KanCell effectively captures non-linear relationships and optimizes computational efficiency through KAN,providing an accurate and efficient tool for ST.By improving data accuracy and resolving cell type composition,KanCell reveals cellular heterogeneity,clarifies disease microenvironments,and identifies therapeutic targets,addressing complex biological challenges.
基金supported by the China National SKA Program(2020SKA0110300)the Natural Science Foundation of China(12433012,12373097)the Guangdong Province Basic and Applied Basic Research Foundation Project of Guangdong Province(2024A1515011503).
文摘The performance of the deconvolution algorithm plays a crucial role in data processing of radio interferometers.The multi-scale multi-frequency synthesis(MSMFS)CLEAN is a widely used deconvolution algorithm for radio interferometric imaging,which combines the advantages of both wide-band synthesis imaging and multi-scale imaging and can substantially improve performance.However,how best to effectively determine the optimal scale is an important problem when implementing the MSMFS CLEAN algorithm.In this study,we proposed a Gaussian fitting method for multiple sources based on the gradient descent algorithm,with consideration of the influence of the point spread function(PSF).After fitting,we analyzed the fitting components using statistical analysis to derive reasonable scale information through the model parameters.A series of simulation validations demonstrated that the scales extracted by our proposed algorithm are accurate and reasonable.The proposed method can be applied to the deconvolution algorithm and provide modeling analysis for Gaussian sources,offering data support for source extraction algorithms.
基金supported by the National Key R&D Program of China[Grant No.2022YFC2402400]the National Natural Science Foundation of China[Grant No.62275062]+2 种基金Project of Shandong Innovation and Startup Community of High-end Medical Apparatus and Instruments[Grant Nos.2023-SGTTXM-002 and 2024-SGTTXM-005]the Shandong Province Technology Innovation Guidance Plan(Central Leading Local Science and Technology Development Fund)[Grant No.YDZX2023115]the Taishan Scholar Special Funding Project of Shandong Province,and the Shandong Laboratory of Advanced Biomaterials and Medical Devices in Weihai[Grant No.ZL202402].
文摘Optical-resolution photoacoustic microscopy is a novel imaging technique that combines the advantages of optical and ultrasound imaging,enabling high-resolution visualization of biological tissues at the micrometer scale.However,the divergence of the excited Gaussian beam limits the depth-of-field of the system to less than 100μm,which hinders accurate three-dimensional imaging of living tissues and restrictsits applicability in biological research.Therefore,there is an urgent need for an effective method to enhance the depth-of-field without altering the hardware configuration.This paper presents a photoacoustic microscopy depth-of-field extension method and system based on three-dimensional continuity and sparsity deconvolution.This method utilizes a depth-varying point spread function and incorporates continuity and sparsity con-straints into the deconvolution process to mitigate the effect of background noise,enhancing the stability and accuracy of the depth-of-field extension.Experimental results using tungsten wire phantoms suggest that the depth-of-field of system can be extended to 650 pm,which is 7.2 times greater than conventional system,while improving the resolution of the defocused region by an average factor of 3.5.Furthermore,experiments on zebrafish and nude mouse ears with irregular topologies demonstrate that the proposed method successfully overcomes image blurring and the loss of structural information due to limited depth-of-field.All the results suggest that the system with higher lateral resolution and enhanced depth-of-field has significant potential for a wide range of practical biomedical applications.
文摘This work focuses on enhancing low frequency seismic data using a convolutional neural network trained on synthetic data.Traditional seismic data often lack both high and low frequencies,which are essential for detailed geological interpretation and various geophysical applications.Low frequency data is particularly valuable for reducing wavelet sidelobes and improving full waveform inversion(FWI).Conventional methods for bandwidth extension include seismic deconvolution and sparse inversion,which have limitations in recovering low frequencies.The study explores the potential of the U-net,which has been successful in other geophysical applications such as noise attenuation and seismic resolution enhancement.The novelty in our approach is that we do not rely on computationally expensive finite difference modelling to create training data.Instead,our synthetic training data is created from individual randomly perturbed events with variations in bandwidth,making it more adaptable to different data sets compared to previous deep learning methods.The method was tested on both synthetic and real seismic data,demonstrating effective low frequency reconstruction and sidelobe reduction.With a synthetic full waveform inversion to recover a velocity model and a seismic amplitude inversion to estimate acoustic impedance we demonstrate the validity and benefit of the proposed method.Overall,the study presents a robust approach to seismic bandwidth extension using deep learning,emphasizing the importance of diverse and well-designed but computationally inexpensive synthetic training data.
基金supported by the basic and forward-looking project(No.2023YQX302)。
文摘Traditional deconvolution methods based on single-channel inversion do not consider the spatial structural relation between channels,and hence,they yield high-resolution results with the existing transverse inconsistency or discontinuity.Therefore,in this study,the local dip angle was used to obtain the structural information and construct the spatial structurally constraint operator.This operator is then introduced into multichannel deconvolution as a regularization operator to improve the resolution and maintain the transverse continuity of seismic data.Model tests and actual seismic data processing have demonstrated the effectiveness and practicability of this method.
基金supported by the Zhanjiang Science and Technology Plan Project(No.2022A05022)Science and Technology Development Special Project of Zhanjiang(No.2023A21616)Research Project of Guangdong Ocean University(No.060302112102).
文摘Double perovskite matrix materials have recently attracted considerable interest due to their structural flexibility,ease of doping,and excellent thermal stability.While photoluminescence(PL)studies of rare-earth-doped double perovskites are common,research on their thermoluminescence(TL)properties is less extensive.This study synthesized a series of Y_(2-x)Sm_(x)MgTiO_(6)(0≤x≤0.1)samples using a high-temperature solid-state method.X-ray diffraction(XRD)analysis confirmed a monoclinic crystal structure(space group P2_(1)∕n),with Sm^(3+)ions substituting for Y^(3+)ions in Y_(2)MgTiO_(6).The PL results indicated that the optimal doping concentration was Y_(1.95)Sm_(0.05)MgTiO_(6),exhibiting emission peaks at 568,605,652,and 715 nm under 409 nm blue light excitation.The TL measurements for different doping concentrations showed that the Y_(1.98)Sm_(0.02)MgTiO_(6)phosphors exhibited the strongest TL signals.The TL peaks observed at 530 and 610 K correspond to defects in the matrix and Sm^(3+)dopants,respectively.The T_(m)-T_(stop)analysis revealed that the TL curve of Y_(1.98)Sm_(0.02)MgTiO_(6)phosphors was a superposition of seven peaks.Computerized glow curve deconvolution(CGCD)was performed on the TL of the sample according to the results of three-dimensional thermoluminescence spectra(3D-TL)and T_(m)-T_(stop),and the trap depths in the sample were estimated to range from 0.69 to 1.49 eV.Additionally,the lifetimes of each overlapping peak were calculated using the fitting parameters.Furthermore,the dose-response test showed that the saturation dose of the sample was high(9956 Gy).Therefore,this material can serve as a thermoluminescent dosimeter for high-dose measurements.The saturation dose for the lowest-temperature overlapping peak was 102 Gy,which correlated with its specific energy-level lifetime,whereas the other overlapping peaks also exhibited favorable linear relationships.
基金financially supported by the National Science and Technology Major Project of China(No.2011ZX05023-005-005)the National Natural Science Foundation of China(No.41274137)
文摘The conventional nonstationary convolutional model assumes that the seismic signal is recorded at normal incidence. Raw shot gathers are far from this assumption because of the effects of offsets. Because of such problems, we propose a novel prestack nonstationary deconvolution approach. We introduce the radial trace （RT） transform to the nonstationary deconvolution, we estimate the nonstationary deconvolution factor with hyperbolic smoothing based on variable-step sampling （VSS） in the RT domain, and we obtain the high-resolution prestack nonstationary deconvolution data. The RT transform maps the shot record from the offset and traveltime coordinates to those of apparent velocity and traveltime. The ray paths of the traces in the RT better satisfy the assumptions of the convolutional model. The proposed method combines the advantages of stationary deconvolution and inverse Q filtering, without prior information for Q. The nonstationary deconvolution in the RT domain is more suitable than that in the space-time （XT） domain for prestack data because it is the generalized extension of normal incidence. Tests with synthetic and real data demonstrate that the proposed method is more effective in compensating for large-offset and deep data.
基金funded by the National Basic Research Program of China(973 Program)(Grant No.2011CB201100)Major Program of the National Natural Science Foundation of China(Grant No.2011ZX05004003)
文摘The Robinson convolution model is mainly restricted by three inappropriate assumptions, i.e., statistically white reflectivity, minimum-phase wavelet, and stationarity. Modern reflectivity inversion methods（e.g., sparsity-constrained deconvolution） generally attempt to suppress the problems associated with the first two assumptions but often ignore that seismic traces are nonstationary signals, which undermines the basic assumption of unchanging wavelet in reflectivity inversion. Through tests on reflectivity series, we confirm the effects of nonstationarity on reflectivity estimation and the loss of significant information, especially in deep layers. To overcome the problems caused by nonstationarity, we propose a nonstationary convolutional model, and then use the attenuation curve in log spectra to detect and correct the influences of nonstationarity. We use Gabor deconvolution to handle nonstationarity and sparsity-constrained deconvolution to separating reflectivity and wavelet. The combination of the two deconvolution methods effectively handles nonstationarity and greatly reduces the problems associated with the unreasonable assumptions regarding reflectivity and wavelet. Using marine seismic data, we show that correcting nonstationarity helps recover subtle reflectivity information and enhances the characterization of details with respect to the geological record.
基金supported by the National High Technology Research and Development Program of China(No.2006AA06A208)
文摘We propose a new automatic method for the interpretation of potential fi eld data, called the RDAS–Euler method, which is based on Euler＇s deconvolution and analytic signal methods. The proposed method can estimate the horizontal and vertical extent of geophysical anomalies without prior information of the nature of the anomalies（structural index）. It also avoids inversion errors because of the erroneous choice of the structural index N in the conventional Euler deconvolution method. The method was tested using model gravity anomalies. In all cases, the misfi t between theoretical values and inversion results is less than 10%. Relative to the conventional Euler deconvolution method, the RDAS–Euler method produces inversion results that are more stable and accurate. Finally, we demonstrate the practicability of the method by applying it to Hulin Basin in Heilongjiang province, where the proposed method produced more accurate data regarding the distribution of faults.
基金With the support of the key project of Knowledge Innovation, CAS(KZCX1-y01, KZCX-SW-18), Fund of the China National Natural Sciences and the Daqing Oilfield with Grant No. 49894190
文摘In seismic data processing, blind deconvolution is a key technology. Introduced in this paper is a flow of one kind of blind deconvolution. The optimal precondition conjugate gradients (PCG) in Kyrlov subspace is also used to improve the stability of the algorithm. The computation amount is greatly decreased.
基金supported by the national project"Deep Exploration Technology and Experimentation"(SinoProbe-09-02)
文摘In order to suppress the airwave noise in marine controlled-source electromagnetic （CSEM） data, we propose a 3D deconvolution （3DD） interferometry method with a synthetic aperture source and obtain the relative anomaly coefficient （RAC） of the EM field reflection responses to show the degree for suppressing the airwave. We analyze the potential of the proposed method for suppressing the airwave, and compare the proposed method with traditional methods in their effectiveness. A method to select synthetic source length is derived and the effect of the water depth on RAC is examined via numerical simulations. The results suggest that 3DD interferometry method with a synthetic source can effectively suppress the airwave and enhance the potential of marine CSEM to hydrocarbon exploration.
基金supported by the Major Basic Research Development Program of China (973 Program)(No.2013CB228606)the National Science foundation of China (No.41174117)+1 种基金the National Major Science-Technology Project (No.2011ZX05031-001)Innovation Fund of PetroChina (No.2010D-5006-0301)
文摘Sparsity constrained deconvolution can improve the resolution of band-limited seismic data compared to conventional deconvolution. However, such deconvolution methods result in nonunique solutions and suppress weak reflections. The Cauchy function, modified Cauchy function, and Huber function are commonly used constraint criteria in sparse deconvolution. We used numerical experiments to analyze the ability of sparsity constrained deconvolution to restore reflectivity sequences and protect weak reflections under different constraint criteria. The experimental results demonstrate that the performance of sparsity constrained deconvolution depends on the agreement between the constraint criteria and the probability distribution of the reflectivity sequences; furthermore, the modified Cauchy- constrained criterion protects the weak reflections better than the other criteria. Based on the model experiments, the probability distribution of the reflectivity sequences of carbonate and clastic formations is statistically analyzed by using well-logging data and then the modified Cauchy-constrained deconvolution is applied to real seismic data much improving the resolution.
基金National 863 Foundation of China(No.2006AA09A102-10)National Natural Science Foundation of China(No.40874056)NCET Fund
文摘The predictive deconvolution algorithm （PD）, which is based on second-order statistics, assumes that the primaries and the multiples are implicitly orthogonal. However, the seismic data usually do not satisfy this assumption in practice. Since the seismic data （primaries and multiples） have a non-Gaussian distribution, in this paper we present an improved predictive deconvolution algorithm （IPD） by maximizing the non-Gaussianity of the recovered primaries. Applications of the IPD method on synthetic and real seismic datasets show that the proposed method obtains promising results.
基金The Scientific Research Foundation of Nanjing University of Posts and Telecommunications(No.NY210049)
文摘A novel algorithm, i.e. the fast alternating direction method of multipliers （ADMM）, is applied to solve the classical total-variation （ TV ）-based model for image reconstruction. First, the TV-based model is reformulated as a linear equality constrained problem where the objective function is separable. Then, by introducing the augmented Lagrangian function, the two variables are alternatively minimized by the Gauss-Seidel idea. Finally, the dual variable is updated. Because the approach makes full use of the special structure of the problem and decomposes the original problem into several low-dimensional sub-problems, the per iteration computational complexity of the approach is dominated by two fast Fourier transforms. Elementary experimental results indicate that the proposed approach is more stable and efficient compared with some state-of-the-art algorithms.
文摘This paper presents a new blind XPIC and a new adaptive blind deconvolutional algorithm based on HOS processing, which separates and equalizes the signals in real time. The simulation results demonstrate that the performance of the proposed adaptive blind algorithm,compared with the conventional algorithms, is outstanding with the feature of feasibility, stability and fast convergence rate.
