Water-fat separation is a particularly important problem for magnetic resonance imaging.Although many methods have been proposed,the reliability is still challenging.In this work,we have presented a method based on th...Water-fat separation is a particularly important problem for magnetic resonance imaging.Although many methods have been proposed,the reliability is still challenging.In this work,we have presented a method based on the combination of the branch-cut method and multigrid algorithm to get a more robust performance of water-fat separation.First,the branch-cut method is applied to identify residues,which violates the requirement that the interacting phase gradient around a closed path be zero.Residues and branches are marked to be zeros and filled to the weighting factor array.Then,the unwrapped phase array can be given by the multigrid algorithm.Finally,the Dixon method for water-fat separation is applied to the unwrapped phase array.Experiments for brain scanning on the 0.3T low field MRI system demonstrate the successful application of the proposed method.展开更多
Reflection imaging results generally reveal large-scale continuous geological information,and it is difficult to identify small-scale geological bodies such as breakpoints,pinch points,small fault blocks,caves,and fra...Reflection imaging results generally reveal large-scale continuous geological information,and it is difficult to identify small-scale geological bodies such as breakpoints,pinch points,small fault blocks,caves,and fractures,etc.Diffraction imaging is an important method to identify small-scale geological bodies and it has higher resolution than reflection imaging.In the common-offset domain,reflections are mostly expressed as smooth linear events,whereas diffractions are characterized by hyperbolic events.This paper proposes a diffraction extraction method based on double sparse transforms.The linear events can be sparsely expressed by the high-resolution linear Radon transform,and the curved events can be sparsely expressed by the Curvelet transform.A sparse inversion model is built and the alternating direction method is used to solve the inversion model.Simulation data and field data experimental results proved that the diffractions extraction method based on double sparse transforms can effectively improve the imaging quality of faults and other small-scale geological bodies.展开更多
Separation of arteries and veins in the cerebral cortex is of significant importance in the studies of cortical hemodynamics,such as the changes of cerebral blood flow,perfusion or oxygen con-centration in arteries an...Separation of arteries and veins in the cerebral cortex is of significant importance in the studies of cortical hemodynamics,such as the changes of cerebral blood flow,perfusion or oxygen con-centration in arteries and veins under different pathological and physiological conditions.Yet the cerebral vessel segmentation and vessel-type separation are challenging due to the complexity of cortical vessel characteristics and low spatial signal-to-noise ratio.In this work,we presented an effective full-field method to differentiate arteries and veins in cerebral cortex using dual-modal optical imaging technology including laser speckle imaging(LSI)and optical intrinsic signals(OIS)imaging.The raw contrast images were acquired by LSI and processed with enhanced laser speckle contrast analysis(eLASCA),algorithm.The vascular pattern was extracted and seg-mented using region growing algorithm from the eLASCA-based LSI.Meanwhile,OIS imageswere acquired altermatively with 630 and 870 nm to obtain an oxy hemoglobin concentration mapover cerebral cortex.Then the separation of arteries and veins was accomplished by Otsuthreshold segmentation algorithm based on the OIS information and segmentation of LSI.Finally,the segmentation and separation performances were assessed using area overlap measure(AOM).The segmentation and separation of cerebral vessels in cortical optical imaging have great potential applications in full-field cerebral hemodynamics monitoring and pathological study of cerebral vascular diseases,as well as in clinical intraoperative monitoring.展开更多
In many image analysis and processing problems, discriminating the size and shape of each individual object in an aggregate pile projected in an image is an important practice. It is relatively easy to distinguish the...In many image analysis and processing problems, discriminating the size and shape of each individual object in an aggregate pile projected in an image is an important practice. It is relatively easy to distinguish these features among the objects already separated from each other. The problems will be undoubtedly more complex and of greater challenge if the objects are touched or/and overlapped. This letter presents an algorithm that can be used to separate the touches and overlaps existing in the objects within a 2-D image. The approach is first to convert the gray-scale image to its corresponding binary one and then to the 3-D topographic one using the erosion operations. A template (or mask) is engineered to search the topographic surface for the saddle point, from which the segmenting orientation is determined followed by the desired separating operation. The algorithm is tested on a real image and the running result is adequately satisfying and encouraging.展开更多
A novel single-channel blind separation algorithm for permuted motion blurred images is proposed by using blind restoration in this paper. Both the motion direction and the length of the point spread function （PSF） ...A novel single-channel blind separation algorithm for permuted motion blurred images is proposed by using blind restoration in this paper. Both the motion direction and the length of the point spread function （PSF） are estimated by Radon transformation and extrema a detection. Using the estimated blur parameters, the permuted image is restored by performing the L-R blind restoration method. The permutation mixing matrices can be accurately estimated by classifying the ringing effect in the restored image, thereby the source images can be separated. Simulation results show a better separation efficiency for the permuted motion blurred image with various permutation operations. The proposed algorithm indicates a better performance on the robustness against Gaussian noise and lossy JPEG compression.展开更多
Objective This study aimed to develop and test a model for predicting dysthyroid optic neuropathy(DON)based on clinical factors and imaging markers of the optic nerve and cerebrospinal fluid(CSF)in the optic nerve she...Objective This study aimed to develop and test a model for predicting dysthyroid optic neuropathy(DON)based on clinical factors and imaging markers of the optic nerve and cerebrospinal fluid(CSF)in the optic nerve sheath.Methods This retrospective study included patients with thyroid-associated ophthalmopathy(TAO)without DON and patients with TAO accompanied by DON at our hospital.The imaging markers of the optic nerve and CSF in the optic nerve sheath were measured on the water-fat images of each patient and,together with clinical factors,were screened by Least absolute shrinkage and selection operator.Subsequently,we constructed a prediction model using multivariate logistic regression.The accuracy of the model was verified using receiver operating characteristic curve analysis.Results In total,80 orbits from 44 DON patients and 90 orbits from 45 TAO patients were included in our study.Two variables(optic nerve subarachnoid space and the volume of the CSF in the optic nerve sheath)were found to be independent predictive factors and were included in the prediction model.In the development cohort,the mean area under the curve(AUC)was 0.994,with a sensitivity of 0.944,specificity of 0.967,and accuracy of 0.901.Moreover,in the validation cohort,the AUC was 0.960,the sensitivity was 0.889,the specificity was 0.893,and the accuracy was 0.890.Conclusions A combined model was developed using imaging data of the optic nerve and CSF in the optic nerve sheath,serving as a noninvasive potential tool to predict DON.展开更多
Fracture-cave reservoirs in carbonate rocks are characterized by a large difference in fracture and cavity size,and a sharp variation in lithology and velocity,thereby resulting in complex diffraction responses.Some s...Fracture-cave reservoirs in carbonate rocks are characterized by a large difference in fracture and cavity size,and a sharp variation in lithology and velocity,thereby resulting in complex diffraction responses.Some small-scale fractures and caves cause weak diffraction energy and would be obscured by the continuous reflection layer in the imaging section,thereby making them difficult to identify.This paper develops a diffraction wave imaging method in the dip domain,which can improve the resolution of small-scale diffractors in the imaging section.Common imaging gathers(CIGs)in the dip domain are extracted by Gaussian beam migration.In accordance with the geometric differences of the diffraction being quasilinear and the reflection being quasiparabolic in the dip-domain CIGs,we use slope analysis technique to filter waves and use Hanning window function to improve the diffraction wave separation level.The diffraction dip-domain CIGs are stacked horizontally to obtain diffraction imaging results.Wavefield separation analysis and numerical modeling results show that the slope analysis method,together with Hanning window filtering,can better suppress noise to obtain the diffraction dip-domain CIGs,thereby improving the clarity of the diffractors in the diffraction imaging section.展开更多
To over come the drawbacks existing in current measurement methods for detecting and controlling colors in printing process, a new medal for color separation and dot recognition is proposed from a view of digital imag...To over come the drawbacks existing in current measurement methods for detecting and controlling colors in printing process, a new medal for color separation and dot recognition is proposed from a view of digital image processing and patter recognition. In this model, firstly data samples are collected from some color patches by the Fuzzy C-Means （FCM） method; then a classifier based on the Cerebellar Model Articulation Controller （CMAC） is constructed which is used to recognize color pattern of each pixel in a microscopic halftone image. The principle of color separation and the algorithm model are introduced and the experiments show the effectiveness of the CMAC-based classifier as opposed to the BP network.展开更多
In this paper, we theoretically and numerically study a combined monopole–dipole measurement mode to show its capability to overcome the issues encountered in conventional single-well imaging, i.e., the low signal-to...In this paper, we theoretically and numerically study a combined monopole–dipole measurement mode to show its capability to overcome the issues encountered in conventional single-well imaging, i.e., the low signal-to-noise ratio of the reflections and azimuth ambiguity. First, the azimuth ambiguity, which exists extensively in conventional single-well imaging, is solved with an improved imaging procedure using combined monopole–dipole logging data in addition to conventional logging data. Furthermore, we demonstrate that the direct waves propagating along the boreholes with strong energy, can be effectively eliminated with the proposed combined monopole–dipole measurement mode. The reflections are therefore predominant in the combined monopole–dipole data even before the signals are filtered; thus, the reflections' arrival times in each receiver are identified, which may help minimize the difficulties in filtering conventional logging data. The optimized processing flow of the combined measurement mode logging image is given in this paper. The proposed combined monopole–dipole measurement mode may improve the accuracy of single-well imaging.展开更多
Diffracted seismic waves may be used to help identify and track geologically heterogeneous bodies or zones.However,the energy of diffracted waves is weaker than that of reflections.Therefore,the extraction of diffract...Diffracted seismic waves may be used to help identify and track geologically heterogeneous bodies or zones.However,the energy of diffracted waves is weaker than that of reflections.Therefore,the extraction of diffracted waves is the basis for the effective utilization of diffracted waves.Based on the difference in travel times between diffracted and reflected waves,we developed a method for separating the diffracted waves via singular value decomposition filters and presented an effective processing flowchart for diffracted wave separation and imaging.The research results show that the horizontally coherent difference between the reflected and diffracted waves can be further improved using normal move-out(NMO) correction.Then,a band-rank or high-rank approximation is used to suppress the reflected waves with better transverse coherence.Following,separation of reflected and diffracted waves is achieved after the filtered data are transformed into the original data domain by inverse NMO.Synthetic and field examples show that our proposed method has the advantages of fewer constraints,fast processing speed and complete extraction of diffracted waves.And the diffracted wave imaging results can effectively improve the identification accuracy of geological heterogeneous bodies or zones.展开更多
The flow field of pulsing air separation is normally in an unsteady turbulence state.With the application of the basic principles of multiphase turbulent flows,we established the dynamical computational model,which sh...The flow field of pulsing air separation is normally in an unsteady turbulence state.With the application of the basic principles of multiphase turbulent flows,we established the dynamical computational model,which shows a remarkable variation of the unstable pulsing air flow field.CFD(computational fluid dynamics) was used to conduct the numerical simulation of the actual geometric model of the classifier.The inside velocity of the flowing fields was analyzed later.The simulation results indicate that the designed structure of the active pulsing air classifier provided a favorable environment for the separation of the particles with different physical characters by density.We shot the movement behaviors of the typical tracer grains in the active pulsing flow field using a high speed dynamic camera.The displacement and velocity curves of the particles in the continuous impulse periods were then analyzed.The experimental results indicate that the effective separation by density of the particles with the same settling velocity and different ranges of the density and particle size can be achieved in the active pulsing airflow field.The experimental results provide an agreement with the simulation results.展开更多
Separation of the components of rigid acoustic scattering by underwater objects is essential in obtaining the structural characteristics of such objects. To overcome the problem of rigid structures appearing to have t...Separation of the components of rigid acoustic scattering by underwater objects is essential in obtaining the structural characteristics of such objects. To overcome the problem of rigid structures appearing to have the same spectral structure in the time domain, time-frequency Blind Source Separation （BSS） can be used in combination with image morphology to separate the rigid scattering components of different objects. Based on a highlight model, the separation of the rigid scattering structure of objects with time-frequency distribution is deduced. Using a morphological filter, different characteristics in a Wigner-Ville Distribution （WVD） observed for single auto term and cross terms can be simplified to remove any cross-term interference. By selecting time and frequency points of the auto terms signal, the accuracy of BSS can be improved. A simulation experimental has been used to analyze the feasibility of the new method, with changing the pulse width of the transmitted signal, the relative amplitude and the time delay parameter. And simulation results show that the new method can not only separate rigid scattering components, but can also separate the components when elastic scattering and rigid scattering exist at the same time. Experimental results confirm that the new method can be used in separating the rigid scattering structure of underwater objects.展开更多
The conventional fast converted-wave imaging method directly uses backward Pand converted S-wavefield to produce joint images. However, this image is accompanied by strong background noises, because the wavefi elds in...The conventional fast converted-wave imaging method directly uses backward Pand converted S-wavefield to produce joint images. However, this image is accompanied by strong background noises, because the wavefi elds in all propagation directions contribute to it. Given this issue, we improve the conventional imaging method in the two aspects. First, the amplitude-preserved P-and S-wavef ield are obtained by using an improved space-domain wavef ield separation scheme to decouple the original elastic wavef ield. Second, a convertedwave imaging condition is constructed based on the directional-wavefield separation and only the wavefields propagating in the same directions used for cross-correlation imaging, resulting in effectively eliminating the imaging artifacts of the wavefields with different directions;Complex-wavefi eld extrapolation is adopted to decompose the decoupled P-and S-wavefield into directional-wavefields during backward propagation, this improves the eff iciency of the directional-wavef ield separation. Experiments on synthetic data show that the improved method generates more accurate converted-wave images than the conventional one. Moreover, the improved method has application potential in micro-seismic and passive-source exploration due to its source-independent characteristic.展开更多
The cell overlapping and adhesion phenomenon often exists in cell image processing. Separating overlapped cell into single ones is of great important and difficult in cell image quantitative analysis and automatic rec...The cell overlapping and adhesion phenomenon often exists in cell image processing. Separating overlapped cell into single ones is of great important and difficult in cell image quantitative analysis and automatic recognition. In this paper, an algorithm based on concave region extraction and erosion limit has been proposed to judge and separate overlapping cell images. Experimental results show that the proposed algorithm has a good separation effects on analog cell images. Then the method is applying in actual cervical cell image and obtains good separation result.展开更多
Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware reso...Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware resources. To address this issue, the MobileNetV1 network was developed, which employs depthwise convolution to reduce network complexity. MobileNetV1 employs a stride of 2 in several convolutional layers to decrease the spatial resolution of feature maps, thereby lowering computational costs. However, this stride setting can lead to a loss of spatial information, particularly affecting the detection and representation of smaller objects or finer details in images. To maintain the trade-off between complexity and model performance, a lightweight convolutional neural network with hierarchical multi-scale feature fusion based on the MobileNetV1 network is proposed. The network consists of two main subnetworks. The first subnetwork uses a depthwise dilated separable convolution (DDSC) layer to learn imaging features with fewer parameters, which results in a lightweight and computationally inexpensive network. Furthermore, depthwise dilated convolution in DDSC layer effectively expands the field of view of filters, allowing them to incorporate a larger context. The second subnetwork is a hierarchical multi-scale feature fusion (HMFF) module that uses parallel multi-resolution branches architecture to process the input feature map in order to extract the multi-scale feature information of the input image. Experimental results on the CIFAR-10, Malaria, and KvasirV1 datasets demonstrate that the proposed method is efficient, reducing the network parameters and computational cost by 65.02% and 39.78%, respectively, while maintaining the network performance compared to the MobileNetV1 baseline.展开更多
基金supported by the National Natural Science Foundation of China(No.60772147,60871060)
文摘Water-fat separation is a particularly important problem for magnetic resonance imaging.Although many methods have been proposed,the reliability is still challenging.In this work,we have presented a method based on the combination of the branch-cut method and multigrid algorithm to get a more robust performance of water-fat separation.First,the branch-cut method is applied to identify residues,which violates the requirement that the interacting phase gradient around a closed path be zero.Residues and branches are marked to be zeros and filled to the weighting factor array.Then,the unwrapped phase array can be given by the multigrid algorithm.Finally,the Dixon method for water-fat separation is applied to the unwrapped phase array.Experiments for brain scanning on the 0.3T low field MRI system demonstrate the successful application of the proposed method.
基金supported by National Natural Science Foundation of China(41974166)Natural Science Foundation of Hebei Province(D2019403082,D2021403010)+1 种基金Hebei Province“three-threethree talent project”(A202005009)Funding for the Science and Technology Innovation Team Project of Hebei GEO University(KJCXTD202106)
文摘Reflection imaging results generally reveal large-scale continuous geological information,and it is difficult to identify small-scale geological bodies such as breakpoints,pinch points,small fault blocks,caves,and fractures,etc.Diffraction imaging is an important method to identify small-scale geological bodies and it has higher resolution than reflection imaging.In the common-offset domain,reflections are mostly expressed as smooth linear events,whereas diffractions are characterized by hyperbolic events.This paper proposes a diffraction extraction method based on double sparse transforms.The linear events can be sparsely expressed by the high-resolution linear Radon transform,and the curved events can be sparsely expressed by the Curvelet transform.A sparse inversion model is built and the alternating direction method is used to solve the inversion model.Simulation data and field data experimental results proved that the diffractions extraction method based on double sparse transforms can effectively improve the imaging quality of faults and other small-scale geological bodies.
文摘Separation of arteries and veins in the cerebral cortex is of significant importance in the studies of cortical hemodynamics,such as the changes of cerebral blood flow,perfusion or oxygen con-centration in arteries and veins under different pathological and physiological conditions.Yet the cerebral vessel segmentation and vessel-type separation are challenging due to the complexity of cortical vessel characteristics and low spatial signal-to-noise ratio.In this work,we presented an effective full-field method to differentiate arteries and veins in cerebral cortex using dual-modal optical imaging technology including laser speckle imaging(LSI)and optical intrinsic signals(OIS)imaging.The raw contrast images were acquired by LSI and processed with enhanced laser speckle contrast analysis(eLASCA),algorithm.The vascular pattern was extracted and seg-mented using region growing algorithm from the eLASCA-based LSI.Meanwhile,OIS imageswere acquired altermatively with 630 and 870 nm to obtain an oxy hemoglobin concentration mapover cerebral cortex.Then the separation of arteries and veins was accomplished by Otsuthreshold segmentation algorithm based on the OIS information and segmentation of LSI.Finally,the segmentation and separation performances were assessed using area overlap measure(AOM).The segmentation and separation of cerebral vessels in cortical optical imaging have great potential applications in full-field cerebral hemodynamics monitoring and pathological study of cerebral vascular diseases,as well as in clinical intraoperative monitoring.
基金Suppprted by the Scientific Research Start-up foundation of Ningbo University (No.2004037)Zhejiang Provincial Foundation for Returned Overseas Students and Scholars (No.2004884).
文摘In many image analysis and processing problems, discriminating the size and shape of each individual object in an aggregate pile projected in an image is an important practice. It is relatively easy to distinguish these features among the objects already separated from each other. The problems will be undoubtedly more complex and of greater challenge if the objects are touched or/and overlapped. This letter presents an algorithm that can be used to separate the touches and overlaps existing in the objects within a 2-D image. The approach is first to convert the gray-scale image to its corresponding binary one and then to the 3-D topographic one using the erosion operations. A template (or mask) is engineered to search the topographic surface for the saddle point, from which the segmenting orientation is determined followed by the desired separating operation. The algorithm is tested on a real image and the running result is adequately satisfying and encouraging.
基金Project supported by the National Natural Science Foundation of China (Grant No.60872114)the Shanghai Leading Academic Discipline Project (Grant No.S30108)the Graduate Student Innovation Foundation of Shanghai University (Grant No.SHUCX101086)
文摘A novel single-channel blind separation algorithm for permuted motion blurred images is proposed by using blind restoration in this paper. Both the motion direction and the length of the point spread function （PSF） are estimated by Radon transformation and extrema a detection. Using the estimated blur parameters, the permuted image is restored by performing the L-R blind restoration method. The permutation mixing matrices can be accurately estimated by classifying the ringing effect in the restored image, thereby the source images can be separated. Simulation results show a better separation efficiency for the permuted motion blurred image with various permutation operations. The proposed algorithm indicates a better performance on the robustness against Gaussian noise and lossy JPEG compression.
基金supported financially by grants from the National Natural Science Foundation of China(No.81771793).
文摘Objective This study aimed to develop and test a model for predicting dysthyroid optic neuropathy(DON)based on clinical factors and imaging markers of the optic nerve and cerebrospinal fluid(CSF)in the optic nerve sheath.Methods This retrospective study included patients with thyroid-associated ophthalmopathy(TAO)without DON and patients with TAO accompanied by DON at our hospital.The imaging markers of the optic nerve and CSF in the optic nerve sheath were measured on the water-fat images of each patient and,together with clinical factors,were screened by Least absolute shrinkage and selection operator.Subsequently,we constructed a prediction model using multivariate logistic regression.The accuracy of the model was verified using receiver operating characteristic curve analysis.Results In total,80 orbits from 44 DON patients and 90 orbits from 45 TAO patients were included in our study.Two variables(optic nerve subarachnoid space and the volume of the CSF in the optic nerve sheath)were found to be independent predictive factors and were included in the prediction model.In the development cohort,the mean area under the curve(AUC)was 0.994,with a sensitivity of 0.944,specificity of 0.967,and accuracy of 0.901.Moreover,in the validation cohort,the AUC was 0.960,the sensitivity was 0.889,the specificity was 0.893,and the accuracy was 0.890.Conclusions A combined model was developed using imaging data of the optic nerve and CSF in the optic nerve sheath,serving as a noninvasive potential tool to predict DON.
基金funded jointly by the National Natural Science Foundation of China(No.41104069)Shandong Province Higher Educational Science and Technology Program(No.J17KA197)+1 种基金Open Foundation of Shandong Provincial Key Laboratory of Depositional Mineralization&Sedimentary Minerals of Shandong University of Science and Technology(No.DMSM2018018)Chunhui Research Foundation of Shengli College,China University of Petroleum(No.KY2017007)。
文摘Fracture-cave reservoirs in carbonate rocks are characterized by a large difference in fracture and cavity size,and a sharp variation in lithology and velocity,thereby resulting in complex diffraction responses.Some small-scale fractures and caves cause weak diffraction energy and would be obscured by the continuous reflection layer in the imaging section,thereby making them difficult to identify.This paper develops a diffraction wave imaging method in the dip domain,which can improve the resolution of small-scale diffractors in the imaging section.Common imaging gathers(CIGs)in the dip domain are extracted by Gaussian beam migration.In accordance with the geometric differences of the diffraction being quasilinear and the reflection being quasiparabolic in the dip-domain CIGs,we use slope analysis technique to filter waves and use Hanning window function to improve the diffraction wave separation level.The diffraction dip-domain CIGs are stacked horizontally to obtain diffraction imaging results.Wavefield separation analysis and numerical modeling results show that the slope analysis method,together with Hanning window filtering,can better suppress noise to obtain the diffraction dip-domain CIGs,thereby improving the clarity of the diffractors in the diffraction imaging section.
文摘To over come the drawbacks existing in current measurement methods for detecting and controlling colors in printing process, a new medal for color separation and dot recognition is proposed from a view of digital image processing and patter recognition. In this model, firstly data samples are collected from some color patches by the Fuzzy C-Means （FCM） method; then a classifier based on the Cerebellar Model Articulation Controller （CMAC） is constructed which is used to recognize color pattern of each pixel in a microscopic halftone image. The principle of color separation and the algorithm model are introduced and the experiments show the effectiveness of the CMAC-based classifier as opposed to the BP network.
基金supported by the National Natural Science Foundation of China(Nos.11574347,11374322,11134011,11734017,and 91630309)PetroChina Innovation Foundation(No.2016D-5007-0304)
文摘In this paper, we theoretically and numerically study a combined monopole–dipole measurement mode to show its capability to overcome the issues encountered in conventional single-well imaging, i.e., the low signal-to-noise ratio of the reflections and azimuth ambiguity. First, the azimuth ambiguity, which exists extensively in conventional single-well imaging, is solved with an improved imaging procedure using combined monopole–dipole logging data in addition to conventional logging data. Furthermore, we demonstrate that the direct waves propagating along the boreholes with strong energy, can be effectively eliminated with the proposed combined monopole–dipole measurement mode. The reflections are therefore predominant in the combined monopole–dipole data even before the signals are filtered; thus, the reflections' arrival times in each receiver are identified, which may help minimize the difficulties in filtering conventional logging data. The optimized processing flow of the combined measurement mode logging image is given in this paper. The proposed combined monopole–dipole measurement mode may improve the accuracy of single-well imaging.
基金supported by the National Natural Science Foundation of China(41874123)Shaanxi Province Natural Science Basic Research Project(2017JZ007)PetroChina Innovation Foundation(2014D-5006-0303)。
文摘Diffracted seismic waves may be used to help identify and track geologically heterogeneous bodies or zones.However,the energy of diffracted waves is weaker than that of reflections.Therefore,the extraction of diffracted waves is the basis for the effective utilization of diffracted waves.Based on the difference in travel times between diffracted and reflected waves,we developed a method for separating the diffracted waves via singular value decomposition filters and presented an effective processing flowchart for diffracted wave separation and imaging.The research results show that the horizontally coherent difference between the reflected and diffracted waves can be further improved using normal move-out(NMO) correction.Then,a band-rank or high-rank approximation is used to suppress the reflected waves with better transverse coherence.Following,separation of reflected and diffracted waves is achieved after the filtered data are transformed into the original data domain by inverse NMO.Synthetic and field examples show that our proposed method has the advantages of fewer constraints,fast processing speed and complete extraction of diffracted waves.And the diffracted wave imaging results can effectively improve the identification accuracy of geological heterogeneous bodies or zones.
基金the financial support provided by the National Natural Science Foundation of China (No.51074156)the Natural Science Foundation of China for InnovativeResearch Group (No. 50921002)+1 种基金the Natural Science Foundation of Jiangsu Province of China (No. BK2010002)the Fundamental Research Funds for the Central Universities (No. 2010ZDP01A06)
文摘The flow field of pulsing air separation is normally in an unsteady turbulence state.With the application of the basic principles of multiphase turbulent flows,we established the dynamical computational model,which shows a remarkable variation of the unstable pulsing air flow field.CFD(computational fluid dynamics) was used to conduct the numerical simulation of the actual geometric model of the classifier.The inside velocity of the flowing fields was analyzed later.The simulation results indicate that the designed structure of the active pulsing air classifier provided a favorable environment for the separation of the particles with different physical characters by density.We shot the movement behaviors of the typical tracer grains in the active pulsing flow field using a high speed dynamic camera.The displacement and velocity curves of the particles in the continuous impulse periods were then analyzed.The experimental results indicate that the effective separation by density of the particles with the same settling velocity and different ranges of the density and particle size can be achieved in the active pulsing airflow field.The experimental results provide an agreement with the simulation results.
基金Foundation item： Supported by the National Natural Science Foundation of China under Grant No. 51279033, and Heilongjiang Natural Science Foundation of China under Grant No. F201346
文摘Separation of the components of rigid acoustic scattering by underwater objects is essential in obtaining the structural characteristics of such objects. To overcome the problem of rigid structures appearing to have the same spectral structure in the time domain, time-frequency Blind Source Separation （BSS） can be used in combination with image morphology to separate the rigid scattering components of different objects. Based on a highlight model, the separation of the rigid scattering structure of objects with time-frequency distribution is deduced. Using a morphological filter, different characteristics in a Wigner-Ville Distribution （WVD） observed for single auto term and cross terms can be simplified to remove any cross-term interference. By selecting time and frequency points of the auto terms signal, the accuracy of BSS can be improved. A simulation experimental has been used to analyze the feasibility of the new method, with changing the pulse width of the transmitted signal, the relative amplitude and the time delay parameter. And simulation results show that the new method can not only separate rigid scattering components, but can also separate the components when elastic scattering and rigid scattering exist at the same time. Experimental results confirm that the new method can be used in separating the rigid scattering structure of underwater objects.
基金supported by the National Science and Technology Major Project of China(No.2017ZX05018-005)National Natural Science Foundation of China(No.41474110)
文摘The conventional fast converted-wave imaging method directly uses backward Pand converted S-wavefield to produce joint images. However, this image is accompanied by strong background noises, because the wavefi elds in all propagation directions contribute to it. Given this issue, we improve the conventional imaging method in the two aspects. First, the amplitude-preserved P-and S-wavef ield are obtained by using an improved space-domain wavef ield separation scheme to decouple the original elastic wavef ield. Second, a convertedwave imaging condition is constructed based on the directional-wavefield separation and only the wavefields propagating in the same directions used for cross-correlation imaging, resulting in effectively eliminating the imaging artifacts of the wavefields with different directions;Complex-wavefi eld extrapolation is adopted to decompose the decoupled P-and S-wavefield into directional-wavefields during backward propagation, this improves the eff iciency of the directional-wavef ield separation. Experiments on synthetic data show that the improved method generates more accurate converted-wave images than the conventional one. Moreover, the improved method has application potential in micro-seismic and passive-source exploration due to its source-independent characteristic.
文摘The cell overlapping and adhesion phenomenon often exists in cell image processing. Separating overlapped cell into single ones is of great important and difficult in cell image quantitative analysis and automatic recognition. In this paper, an algorithm based on concave region extraction and erosion limit has been proposed to judge and separate overlapping cell images. Experimental results show that the proposed algorithm has a good separation effects on analog cell images. Then the method is applying in actual cervical cell image and obtains good separation result.
文摘Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware resources. To address this issue, the MobileNetV1 network was developed, which employs depthwise convolution to reduce network complexity. MobileNetV1 employs a stride of 2 in several convolutional layers to decrease the spatial resolution of feature maps, thereby lowering computational costs. However, this stride setting can lead to a loss of spatial information, particularly affecting the detection and representation of smaller objects or finer details in images. To maintain the trade-off between complexity and model performance, a lightweight convolutional neural network with hierarchical multi-scale feature fusion based on the MobileNetV1 network is proposed. The network consists of two main subnetworks. The first subnetwork uses a depthwise dilated separable convolution (DDSC) layer to learn imaging features with fewer parameters, which results in a lightweight and computationally inexpensive network. Furthermore, depthwise dilated convolution in DDSC layer effectively expands the field of view of filters, allowing them to incorporate a larger context. The second subnetwork is a hierarchical multi-scale feature fusion (HMFF) module that uses parallel multi-resolution branches architecture to process the input feature map in order to extract the multi-scale feature information of the input image. Experimental results on the CIFAR-10, Malaria, and KvasirV1 datasets demonstrate that the proposed method is efficient, reducing the network parameters and computational cost by 65.02% and 39.78%, respectively, while maintaining the network performance compared to the MobileNetV1 baseline.
