To enable proper diagnosis of a patient,medical images must demonstrate no presence of noise and artifacts.The major hurdle lies in acquiring these images in such a manner that extraneous variables,causing distortions...To enable proper diagnosis of a patient,medical images must demonstrate no presence of noise and artifacts.The major hurdle lies in acquiring these images in such a manner that extraneous variables,causing distortions in the form of noise and artifacts,are kept to a bare minimum.The unexpected change realized during the acquisition process specifically attacks the integrity of the image’s quality,while indirectly attacking the effectiveness of the diagnostic process.It is thus crucial that this is attended to with maximum efficiency at the level of pertinent expertise.The solution to these challenges presents a complex dilemma at the acquisition stage,where image processing techniques must be adopted.The necessity of this mandatory image pre-processing step underpins the implementation of traditional state-of-the-art methods to create functional and robust denoising or recovery devices.This article hereby provides an extensive systematic review of the above techniques,with the purpose of presenting a systematic evaluation of their effect on medical images under three different distributions of noise,i.e.,Gaussian,Poisson,and Rician.A thorough analysis of these methods is conducted using eight evaluation parameters to highlight the unique features of each method.The covered denoising methods are essential in actual clinical scenarios where the preservation of anatomical details is crucial for accurate and safe diagnosis,such as tumor detection in MRI and vascular imaging in CT.展开更多
A frequency and spatial domain decomposition method （FSDD） for operational modal analysis （OMA） is presented in this paper, which is an extension of the complex mode indicator function （CMIF） method for experime...A frequency and spatial domain decomposition method （FSDD） for operational modal analysis （OMA） is presented in this paper, which is an extension of the complex mode indicator function （CMIF） method for experimental modal analysis （EMA）. The theoretical background of the FSDD method is clarified, Singular value decomposition is adopted to separate the signal space from the noise space. Finally, an enhanced power spectrum density （PSD） is proposed to obtain more accurate modal parameters by curve fitting in the frequency domain. Moreover, a simulation case and an application case are used to validate this method.展开更多
A guidance policy for controller performance enhancement utilizing mobile sensor-actuator networks （MSANs） is proposed for a class of distributed parameter systems （DPSs）, which are governed by diffusion partial d...A guidance policy for controller performance enhancement utilizing mobile sensor-actuator networks （MSANs） is proposed for a class of distributed parameter systems （DPSs）, which are governed by diffusion partial differential equations （PDEs） with time-dependent spatial domains. Several sufficient conditions for controller performance enhancement are presented. First, the infinite dimensional operator theory is used to derive an abstract evolution equation of the systems under some rational assumptions on the operators, and a static output feedback controller is designed to control the spatial process. Then, based on Lyapunov stability arguments, guidance policies for collocated and non-collocated MSANs are provided to enhance the performance of the proposed controller, which show that the time-dependent characteristic of the spatial domains can significantly affect the design of the mobile scheme. Finally, a simulation example illustrates the effectiveness of the proposed policy.展开更多
This paper presents a spatial domain quantum watermarking scheme. For a quantum watermarking scheme,a feasible quantum circuit is a key to achieve it. This paper gives a feasible quantum circuit for the presented sche...This paper presents a spatial domain quantum watermarking scheme. For a quantum watermarking scheme,a feasible quantum circuit is a key to achieve it. This paper gives a feasible quantum circuit for the presented scheme. In order to give the quantum circuit, a new quantum multi-control rotation gate, which can be achieved with quantum basic gates, is designed. With this quantum circuit, our scheme can arbitrarily control the embedding position of watermark images on carrier images with the aid of auxiliary qubits. Besides reversely acting the given quantum circuit, the paper gives another watermark extracting algorithm based on quantum measurements. Moreover, this paper also gives a new quantum image scrambling method and its quantum circuit. Differ from other quantum watermarking schemes, all given quantum circuits can be implemented with basic quantum gates. Moreover, the scheme is a spatial domain watermarking scheme, and is not based on any transform algorithm on quantum images. Meanwhile, it can make sure the watermark be secure even though the watermark has been found. With the given quantum circuit, this paper implements simulation experiments for the presented scheme. The experimental result shows that the scheme does well in the visual quality and the embedding capacity.展开更多
Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various tissues.Graph-based geometric deep learning has gained widespread adoption for...Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various tissues.Graph-based geometric deep learning has gained widespread adoption for spatial domain identification tasks.Currently,most methods define adjacency relation between cells or spots by their spatial distance in SRT data,which overlooks key biological interactions like gene expression similarities,and leads to inaccuracies in spatial domain identification.To tackle this challenge,we propose a novel method,SpaGRA(https://github.com/sunxue-yy/SpaGRA),for automatic multi-relationship construction based on graph augmentation.SpaGRA uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks(GATs).This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive learning.Additionally,SpaGRA uses these multi-view relationships to construct negative samples,addressing sampling bias posed by random selection.Experimental results show that SpaGRA presents superior domain identification performance on multiple datasets generated from different protocols.Using SpaGRA,we analyze the functional regions in the mouse hypothalamus,identify key genes related to heart development in mouse embryos,and observe cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD data.Overall,SpaGRA can effectively characterize spatial structures across diverse SRT datasets.展开更多
Structured illumination microscopy(SIM)achieves super-resolution(SR)by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction.The traditiona...Structured illumination microscopy(SIM)achieves super-resolution(SR)by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction.The traditional Wiener-filtering-based reconstruction algorithm operates in the Fourier domain,it requires prior knowledge of the sinusoidal illumination patterns which makes the time-consuming procedure of parameter estimation to raw datasets necessary,besides,the parameter estimation is sensitive to noise or aberration-induced pattern distortion which leads to reconstruction artifacts.Here,we propose a spatial-domain image reconstruction method that does not require parameter estimation but calculates patterns from raw datasets,and a reconstructed image can be obtained just by calculating the spatial covariance of differential calculated patterns and differential filtered datasets(the notch filtering operation is performed to the raw datasets for attenuating and compensating the optical transfer function(OTF)).Experiments on reconstructing raw datasets including nonbiological,biological,and simulated samples demonstrate that our method has SR capability,high reconstruction speed,and high robustness to aberration and noise.展开更多
Hiding secret data in digital multimedia has been essential to protect the data.Nevertheless,attackers with a steganalysis technique may break them.Existing steganalysis methods have good results with conventional Mac...Hiding secret data in digital multimedia has been essential to protect the data.Nevertheless,attackers with a steganalysis technique may break them.Existing steganalysis methods have good results with conventional Machine Learning(ML)techniques;however,the introduction of Convolutional Neural Network(CNN),a deep learning paradigm,achieved better performance over the previously proposed ML-based techniques.Though the existing CNN-based approaches yield good results,they present performance issues in classification accuracy and stability in the network training phase.This research proposes a new method with a CNN architecture to improve the hidden data detection accuracy and the training phase stability in spatial domain images.The proposed method comprises three phases:pre-processing,feature extraction,and classification.Firstly,in the pre-processing phase,we use spatial rich model filters to enhance the noise within images altered by data hiding;secondly,in the feature extraction phase,we use two-dimensional depthwise separable convolutions to improve the signal-to-noise and regular convolutions to model local features;and finally,in the classification,we use multi-scale average pooling for local features aggregation and representability enhancement regardless of the input size variation,followed by three fully connected layers to form the final feature maps that we transform into class probabilities using the softmax function.The results identify an improvement in the accuracy of the considered recent scheme ranging between 4.6 and 10.2%with reduced training time up to 30.81%.展开更多
As a highly entangled quantum network, the duster state has the potential for greater information capacity and use in measurement-based quantum computation. Here, we report generating a continuous-variable quadriparti...As a highly entangled quantum network, the duster state has the potential for greater information capacity and use in measurement-based quantum computation. Here, we report generating a continuous-variable quadripartite ＂square＂ cluster state of multiplexing orthogonal spatial modes in a single optical parametric amplifier （OPA）, and further improve the quality of entanglement by optimizing the pump profile. We produce multimode en- tanglement of two first-order Hermite-Gauss modes within one beam in a single multimode OPA and transform it into a duster state by phase correction. Furthermore, the pump-profile dependence of the entanglement of this state is explored. Compared with fundamental mode pumping, an enhancement of approximately 33% is achieved using the suitable pump-profile mode. Our approach is potentially scalable to multimode entanglement in the spatial domain. Such spatial duster states may contribute to future schemes in spatial quantum information processing.展开更多
Based on the dipole source method, all components of the Green＇s functions in spectral domain are restructured concisely by four basis functions, and in terms of the two-level discrete complex image method （DCIM） w...Based on the dipole source method, all components of the Green＇s functions in spectral domain are restructured concisely by four basis functions, and in terms of the two-level discrete complex image method （DCIM） with the high order Sommerfeld identities, an efficient algorithm for closed-form Green＇s functions in spatial domain in multilayered media is presented. This new work enjoys the advantages of the surface wave pole extraction directly carried out by the generalized integral path without troubles of that all components of Green＇s function in spectral domain should be reformed respectively in transmission line network analogy, and then the Green＇s functions for mixed-potential integral equation （MPIE） analysis in both near-field and far-field in multilayered media are obtained. In addition, the curl operator for coupled field in MPIE is avoided conveniently. It is especially applicable and useful to characterize the electromagnetic scattering by, and radiation in the presence of, the electrically large 3-D objects in multilayered media. The numerical results of the S-parameters of a microstrip periodic bandgap （PBG） filter, the radar cross section （RCS） of a large microstrip antenna array, the characteristics of scattering, and radiation from the three-dimensional （3-D） targets in multilayered media are obtained, to demonstrate better effectiveness and accuracy of this technique.展开更多
Imaging quality is a critical component of compressive imaging in real applications. In this study, we propose a compressive imaging method based on multi-scale modulation and reconstruction in the spatial frequency d...Imaging quality is a critical component of compressive imaging in real applications. In this study, we propose a compressive imaging method based on multi-scale modulation and reconstruction in the spatial frequency domain. Theoretical analysis and simulation show the relation between the measurement matrix resolution and compressive sensing(CS)imaging quality. The matrix design is improved to provide multi-scale modulations, followed by individual reconstruction of images of different spatial frequencies. Compared with traditional single-scale CS imaging, the multi-scale method provides high quality imaging in both high and low frequencies, and effectively decreases the overall reconstruction error.Experimental results confirm the feasibility of this technique, especially at low sampling rate. The method may thus be helpful in promoting the implementation of compressive imaging in real applications.展开更多
An improved method of using a selective spatial-domain mask to reduce speckle noise in digital holography is proposed.The sub-holograms are obtained from the original hologram filtered by the binary masks including a ...An improved method of using a selective spatial-domain mask to reduce speckle noise in digital holography is proposed.The sub-holograms are obtained from the original hologram filtered by the binary masks including a shifting aperture for being reconstructed. Normally, the speckle patterns of these sub-reconstructed images are different. The speckle intensity of the final reconstructed image is suppressed by averaging the favorable sub-reconstructed images which are selected based on the most optimal pixel intensity sub-range in the sub-holograms. Compared with the conventional spatial-domain mask method, the proposed method not only reduces the speckle noise more effectively with fewer sub-reconstructed images,but also reduces the redundant information used in the reconstruction process.展开更多
Large span spatial lattice structures have many natural frequencies in a narrow frequency range, the conventional frequency domain method is difficult to contain all significant contribution modes. Through numerical e...Large span spatial lattice structures have many natural frequencies in a narrow frequency range, the conventional frequency domain method is difficult to contain all significant contribution modes. Through numerical examples, it is found that some high order modes are likely to be overlooked because of their higher positions of modal order, in spite of their significance to wind response. According to the contributions of modes to strain energy of system, the paper presented an efficient method to compensate the errors owing to missing out some significant high order modes. The effectiveness of the proposed method is verified through a numerical analysis of the wind responses of a spherical dome.展开更多
心脏磁共振成像(cardiac magnetic resonance,CMR)过程中患者误动、异常幅度的呼吸运动、心律失常会造成CMR图像质量下降,为解决现有的CMR图像增强网络需要人为制作配对数据,且图像增强后部分组织纹理细节丢失的问题,提出了基于空频域...心脏磁共振成像(cardiac magnetic resonance,CMR)过程中患者误动、异常幅度的呼吸运动、心律失常会造成CMR图像质量下降,为解决现有的CMR图像增强网络需要人为制作配对数据,且图像增强后部分组织纹理细节丢失的问题,提出了基于空频域特征学习的循环一致性生成对抗网络(cycle-consistent generative adversavial network based on spatial-frequency domain feature learning,SFFL-CycleGAN).研究结果表明,该网络无须人为制作配对数据集,增强后的CMR图像组织纹理细节丰富,在结构相似度(structural similarity,SSIM)和峰值信噪比(peak signal to noise ratio,PSNR)等方面均优于现有的配对训练网络以及原始的CycleGAN网络,图像增强效果好,有效助力病情诊断.展开更多
文摘To enable proper diagnosis of a patient,medical images must demonstrate no presence of noise and artifacts.The major hurdle lies in acquiring these images in such a manner that extraneous variables,causing distortions in the form of noise and artifacts,are kept to a bare minimum.The unexpected change realized during the acquisition process specifically attacks the integrity of the image’s quality,while indirectly attacking the effectiveness of the diagnostic process.It is thus crucial that this is attended to with maximum efficiency at the level of pertinent expertise.The solution to these challenges presents a complex dilemma at the acquisition stage,where image processing techniques must be adopted.The necessity of this mandatory image pre-processing step underpins the implementation of traditional state-of-the-art methods to create functional and robust denoising or recovery devices.This article hereby provides an extensive systematic review of the above techniques,with the purpose of presenting a systematic evaluation of their effect on medical images under three different distributions of noise,i.e.,Gaussian,Poisson,and Rician.A thorough analysis of these methods is conducted using eight evaluation parameters to highlight the unique features of each method.The covered denoising methods are essential in actual clinical scenarios where the preservation of anatomical details is crucial for accurate and safe diagnosis,such as tumor detection in MRI and vascular imaging in CT.
基金China Postdoctoral Science Foundation Under Grant No. 2004035215 Jiangsu Planned Projects for Postdoctoral Research Funds 2004 Aeronautical Science Research Foundation Under Grant No. 04152065
文摘A frequency and spatial domain decomposition method （FSDD） for operational modal analysis （OMA） is presented in this paper, which is an extension of the complex mode indicator function （CMIF） method for experimental modal analysis （EMA）. The theoretical background of the FSDD method is clarified, Singular value decomposition is adopted to separate the signal space from the noise space. Finally, an enhanced power spectrum density （PSD） is proposed to obtain more accurate modal parameters by curve fitting in the frequency domain. Moreover, a simulation case and an application case are used to validate this method.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61174021 and 61473136)
文摘A guidance policy for controller performance enhancement utilizing mobile sensor-actuator networks （MSANs） is proposed for a class of distributed parameter systems （DPSs）, which are governed by diffusion partial differential equations （PDEs） with time-dependent spatial domains. Several sufficient conditions for controller performance enhancement are presented. First, the infinite dimensional operator theory is used to derive an abstract evolution equation of the systems under some rational assumptions on the operators, and a static output feedback controller is designed to control the spatial process. Then, based on Lyapunov stability arguments, guidance policies for collocated and non-collocated MSANs are provided to enhance the performance of the proposed controller, which show that the time-dependent characteristic of the spatial domains can significantly affect the design of the mobile scheme. Finally, a simulation example illustrates the effectiveness of the proposed policy.
基金Supported by the National Natural Science Foundation of China under Grant Nos.61272514,61170272,61373131,61121061,61411146001the program for New Century Excellent Talents under Grant No.NCET-13-0681+2 种基金the National Development Foundation for Cryptological Research(Grant No.MMJJ201401012)the Fok Ying Tung Education Foundation under Grant No.131067the Shandong Provincial Natural Science Foundation of China under Grant No.ZR2013FM025
文摘This paper presents a spatial domain quantum watermarking scheme. For a quantum watermarking scheme,a feasible quantum circuit is a key to achieve it. This paper gives a feasible quantum circuit for the presented scheme. In order to give the quantum circuit, a new quantum multi-control rotation gate, which can be achieved with quantum basic gates, is designed. With this quantum circuit, our scheme can arbitrarily control the embedding position of watermark images on carrier images with the aid of auxiliary qubits. Besides reversely acting the given quantum circuit, the paper gives another watermark extracting algorithm based on quantum measurements. Moreover, this paper also gives a new quantum image scrambling method and its quantum circuit. Differ from other quantum watermarking schemes, all given quantum circuits can be implemented with basic quantum gates. Moreover, the scheme is a spatial domain watermarking scheme, and is not based on any transform algorithm on quantum images. Meanwhile, it can make sure the watermark be secure even though the watermark has been found. With the given quantum circuit, this paper implements simulation experiments for the presented scheme. The experimental result shows that the scheme does well in the visual quality and the embedding capacity.
基金supported by the National Natural Science Foundation of China(Nos.62303271,U1806202,62103397)the Natural Science Foundation of Shandong Province(ZR2023QF081)Funding for open access charge:the National Natural Science Foundation of China(Nos.62303271,U1806202).
文摘Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various tissues.Graph-based geometric deep learning has gained widespread adoption for spatial domain identification tasks.Currently,most methods define adjacency relation between cells or spots by their spatial distance in SRT data,which overlooks key biological interactions like gene expression similarities,and leads to inaccuracies in spatial domain identification.To tackle this challenge,we propose a novel method,SpaGRA(https://github.com/sunxue-yy/SpaGRA),for automatic multi-relationship construction based on graph augmentation.SpaGRA uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks(GATs).This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive learning.Additionally,SpaGRA uses these multi-view relationships to construct negative samples,addressing sampling bias posed by random selection.Experimental results show that SpaGRA presents superior domain identification performance on multiple datasets generated from different protocols.Using SpaGRA,we analyze the functional regions in the mouse hypothalamus,identify key genes related to heart development in mouse embryos,and observe cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD data.Overall,SpaGRA can effectively characterize spatial structures across diverse SRT datasets.
基金funded by the National Natural Science Foundation of China(62125504,61827825,and 31901059)Zhejiang Provincial Ten Thousand Plan for Young Top Talents(2020R52001)Open Project Program of Wuhan National Laboratory for Optoelectronics(2021WNLOKF007).
文摘Structured illumination microscopy(SIM)achieves super-resolution(SR)by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction.The traditional Wiener-filtering-based reconstruction algorithm operates in the Fourier domain,it requires prior knowledge of the sinusoidal illumination patterns which makes the time-consuming procedure of parameter estimation to raw datasets necessary,besides,the parameter estimation is sensitive to noise or aberration-induced pattern distortion which leads to reconstruction artifacts.Here,we propose a spatial-domain image reconstruction method that does not require parameter estimation but calculates patterns from raw datasets,and a reconstructed image can be obtained just by calculating the spatial covariance of differential calculated patterns and differential filtered datasets(the notch filtering operation is performed to the raw datasets for attenuating and compensating the optical transfer function(OTF)).Experiments on reconstructing raw datasets including nonbiological,biological,and simulated samples demonstrate that our method has SR capability,high reconstruction speed,and high robustness to aberration and noise.
基金supported by the Ministry of Education,Culture,Research and Technology,The Republic of Indonesia,and Institut Teknologi Sepuluh Nopember.
文摘Hiding secret data in digital multimedia has been essential to protect the data.Nevertheless,attackers with a steganalysis technique may break them.Existing steganalysis methods have good results with conventional Machine Learning(ML)techniques;however,the introduction of Convolutional Neural Network(CNN),a deep learning paradigm,achieved better performance over the previously proposed ML-based techniques.Though the existing CNN-based approaches yield good results,they present performance issues in classification accuracy and stability in the network training phase.This research proposes a new method with a CNN architecture to improve the hidden data detection accuracy and the training phase stability in spatial domain images.The proposed method comprises three phases:pre-processing,feature extraction,and classification.Firstly,in the pre-processing phase,we use spatial rich model filters to enhance the noise within images altered by data hiding;secondly,in the feature extraction phase,we use two-dimensional depthwise separable convolutions to improve the signal-to-noise and regular convolutions to model local features;and finally,in the classification,we use multi-scale average pooling for local features aggregation and representability enhancement regardless of the input size variation,followed by three fully connected layers to form the final feature maps that we transform into class probabilities using the softmax function.The results identify an improvement in the accuracy of the considered recent scheme ranging between 4.6 and 10.2%with reduced training time up to 30.81%.
基金National Natural Science Foundation of China(NSFC)(91536222,11674205)Ministry of Science and Technology of the People’s Republic of China(MOST)(2016YFA0301404)+1 种基金Program for the Outstanding Innovative Team of Higher Learning Institution of ShanxiShanxi "1331 Project"
文摘As a highly entangled quantum network, the duster state has the potential for greater information capacity and use in measurement-based quantum computation. Here, we report generating a continuous-variable quadripartite ＂square＂ cluster state of multiplexing orthogonal spatial modes in a single optical parametric amplifier （OPA）, and further improve the quality of entanglement by optimizing the pump profile. We produce multimode en- tanglement of two first-order Hermite-Gauss modes within one beam in a single multimode OPA and transform it into a duster state by phase correction. Furthermore, the pump-profile dependence of the entanglement of this state is explored. Compared with fundamental mode pumping, an enhancement of approximately 33% is achieved using the suitable pump-profile mode. Our approach is potentially scalable to multimode entanglement in the spatial domain. Such spatial duster states may contribute to future schemes in spatial quantum information processing.
基金the National Natural Science Foundation of China (Grant No. 60371020)National Defense Pre-research Foundation of China (Grant No. 9140a03020206dz0112)
文摘Based on the dipole source method, all components of the Green＇s functions in spectral domain are restructured concisely by four basis functions, and in terms of the two-level discrete complex image method （DCIM） with the high order Sommerfeld identities, an efficient algorithm for closed-form Green＇s functions in spatial domain in multilayered media is presented. This new work enjoys the advantages of the surface wave pole extraction directly carried out by the generalized integral path without troubles of that all components of Green＇s function in spectral domain should be reformed respectively in transmission line network analogy, and then the Green＇s functions for mixed-potential integral equation （MPIE） analysis in both near-field and far-field in multilayered media are obtained. In addition, the curl operator for coupled field in MPIE is avoided conveniently. It is especially applicable and useful to characterize the electromagnetic scattering by, and radiation in the presence of, the electrically large 3-D objects in multilayered media. The numerical results of the S-parameters of a microstrip periodic bandgap （PBG） filter, the radar cross section （RCS） of a large microstrip antenna array, the characteristics of scattering, and radiation from the three-dimensional （3-D） targets in multilayered media are obtained, to demonstrate better effectiveness and accuracy of this technique.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61601442,61605218,and 61575207)the National Key Research and Development Program of China(Grant No.2018YFB0504302)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant Nos.2015124 and 2019154)。
文摘Imaging quality is a critical component of compressive imaging in real applications. In this study, we propose a compressive imaging method based on multi-scale modulation and reconstruction in the spatial frequency domain. Theoretical analysis and simulation show the relation between the measurement matrix resolution and compressive sensing(CS)imaging quality. The matrix design is improved to provide multi-scale modulations, followed by individual reconstruction of images of different spatial frequencies. Compared with traditional single-scale CS imaging, the multi-scale method provides high quality imaging in both high and low frequencies, and effectively decreases the overall reconstruction error.Experimental results confirm the feasibility of this technique, especially at low sampling rate. The method may thus be helpful in promoting the implementation of compressive imaging in real applications.
基金Project supported by Guangdong Provincial Science and Technology Plan Project of China(Grant Nos.2015B010114007 and 2014B050505020)
文摘An improved method of using a selective spatial-domain mask to reduce speckle noise in digital holography is proposed.The sub-holograms are obtained from the original hologram filtered by the binary masks including a shifting aperture for being reconstructed. Normally, the speckle patterns of these sub-reconstructed images are different. The speckle intensity of the final reconstructed image is suppressed by averaging the favorable sub-reconstructed images which are selected based on the most optimal pixel intensity sub-range in the sub-holograms. Compared with the conventional spatial-domain mask method, the proposed method not only reduces the speckle noise more effectively with fewer sub-reconstructed images,but also reduces the redundant information used in the reconstruction process.
文摘Large span spatial lattice structures have many natural frequencies in a narrow frequency range, the conventional frequency domain method is difficult to contain all significant contribution modes. Through numerical examples, it is found that some high order modes are likely to be overlooked because of their higher positions of modal order, in spite of their significance to wind response. According to the contributions of modes to strain energy of system, the paper presented an efficient method to compensate the errors owing to missing out some significant high order modes. The effectiveness of the proposed method is verified through a numerical analysis of the wind responses of a spherical dome.
基金National Basic Research Program（973 Program） （No.2011 CB952001） National High Technology Research and Development Program of China （863 Program） （No.2008AA 12 Z 106）+1 种基金 National Natural Science Foundation of China （No.40801166） China Postdoctoral Science Foundation （No.2012M510053 ）
文摘心脏磁共振成像(cardiac magnetic resonance,CMR)过程中患者误动、异常幅度的呼吸运动、心律失常会造成CMR图像质量下降,为解决现有的CMR图像增强网络需要人为制作配对数据,且图像增强后部分组织纹理细节丢失的问题,提出了基于空频域特征学习的循环一致性生成对抗网络(cycle-consistent generative adversavial network based on spatial-frequency domain feature learning,SFFL-CycleGAN).研究结果表明,该网络无须人为制作配对数据集,增强后的CMR图像组织纹理细节丰富,在结构相似度(structural similarity,SSIM)和峰值信噪比(peak signal to noise ratio,PSNR)等方面均优于现有的配对训练网络以及原始的CycleGAN网络,图像增强效果好,有效助力病情诊断.
