Full waveform inversion(FWI)is an extremely important velocity-model-building method.However,it involves a large amount of calculation,which hindsers its practical application.The multi-source technology can reduce th...Full waveform inversion(FWI)is an extremely important velocity-model-building method.However,it involves a large amount of calculation,which hindsers its practical application.The multi-source technology can reduce the number of forward modeling shots during the inversion process,thereby improving the efficiency.However,it introduces crossnoise problems.In this paper,we propose a sparse constrained encoding multi-source FWI method based on K-SVD dictionary learning.The phase encoding technology is introduced to reduce crosstalk noise,whereas the K-SVD dictionary learning method is used to obtain the basis of the transformation according to the characteristics of the inversion results.The multiscale inversion method is adopted to further enhance the stability of FWI.Finally,the synthetic subsag model and the Marmousi model are set to test the effectiveness of the newly proposed method.Analysis of the results suggest the following:(1)The new method can effectively reduce the computational complexity of FWI while ensuring inversion accuracy and stability;(2)The proposed method can be combined with the time-domain multi-scale FWI strategy flexibly to further avoid the local minimum and to improve the stability of inversion,which is of significant importance for the inversion of the complex model.展开更多
Time-domain airborne electromagnetic(AEM)data are frequently subject to interference from various types of noise,which can reduce the data quality and affect data inversion and interpretation.Traditional denoising met...Time-domain airborne electromagnetic(AEM)data are frequently subject to interference from various types of noise,which can reduce the data quality and affect data inversion and interpretation.Traditional denoising methods primarily deal with data directly,without analyzing the data in detail;thus,the results are not always satisfactory.In this paper,we propose a method based on dictionary learning for EM data denoising.This method uses dictionary learning to perform feature analysis and to extract and reconstruct the true signal.In the process of dictionary learning,the random noise is fi ltered out as residuals.To verify the eff ectiveness of this dictionary learning approach for denoising,we use a fi xed overcomplete discrete cosine transform(ODCT)dictionary algorithm,the method-of-optimal-directions(MOD)dictionary learning algorithm,and the K-singular value decomposition(K-SVD)dictionary learning algorithm to denoise decay curves at single points and to denoise profi le data for diff erent time channels in time-domain AEM.The results show obvious diff erences among the three dictionaries for denoising AEM data,with the K-SVD dictionary achieving the best performance.展开更多
The bronze gui food container published in 2007 bears an inscription that records the event of the Zhou king enfeoffing earl Tang in the Jin State and granting him the title "marquis." But the vessel owner &...The bronze gui food container published in 2007 bears an inscription that records the event of the Zhou king enfeoffing earl Tang in the Jin State and granting him the title "marquis." But the vessel owner "■ 公" (Duke ■) has not been identified all along. As the author believes,this character should be taken as" (爻疋) (疏)"seen in the Shuo Wen《说文》because it contains the component "爻"and is read "疋"(夏).展开更多
Parameter estimation of the attributed scattering center(ASC) model is significant for automatic target recognition(ATR). Sparse representation based parameter estimation methods have developed rapidly. Construction o...Parameter estimation of the attributed scattering center(ASC) model is significant for automatic target recognition(ATR). Sparse representation based parameter estimation methods have developed rapidly. Construction of the separable dictionary is a key issue for sparse representation technology. A compressive time-domain dictionary(TD) for ASC model is presented. Two-dimensional frequency domain responses of the ASC are produced and transformed into the time domain. Then these time domain responses are cutoff and stacked into vectors. These vectored time-domain responses are amalgamated to form the TD. Compared with the traditional frequency-domain dictionary(FD), the TD is a matrix that is quite spare and can markedly reduce the data size of the dictionary. Based on the basic TD construction method, we present four extended TD construction methods, which are available for different applications. In the experiments, the performance of the TD, including the basic model and the extended models, has been firstly analyzed in comparison with the FD. Secondly, an example of parameter estimation from SAR synthetic aperture radar(SAR) measurements of a target collected in an anechoic room is exhibited. Finally, a sparse image reconstruction example is from two apart apertures. Experimental results demonstrate the effectiveness and efficiency of the proposed TD.展开更多
The K-COD (K-Complete Orthogonal Decomposition) algorithm for generating adaptive dictionary for signals sparse representation in the framework of K-means clustering is proposed in this paper,in which rank one approxi...The K-COD (K-Complete Orthogonal Decomposition) algorithm for generating adaptive dictionary for signals sparse representation in the framework of K-means clustering is proposed in this paper,in which rank one approximation for components assembling signals based on COD and K-means clustering based on chaotic random search are well utilized. The results of synthetic test and empirical experiment for the real data show that the proposed algorithm outperforms recently reported alternatives: K-Singular Value Decomposition (K-SVD) algorithm and Method of Optimal Directions (MOD) algorithm.展开更多
Recently, low-dose computed tomography (CT) has become highly desirable because of the growing concern for the potential risks of excessive radiation. For low-dose CT imaging, it is a significant challenge to guaran...Recently, low-dose computed tomography (CT) has become highly desirable because of the growing concern for the potential risks of excessive radiation. For low-dose CT imaging, it is a significant challenge to guarantee image quality while reducing radiation dosage. Compared with classical filtered backprojection algorithms, compressed sensing-based iterative re- construction has achieved excellent imaging performance, but its clinical application is hindered due to its computational ineffi- ciency. To promote low-dose CT imaging, we propose a promising reconstruction scheme which combines total-variation mini- mization and sparse dictionary learning to enhance the reconstruction performance, and properly schedule them with an adaptive iteration stopping strategy to boost the reconstruction speed. Experiments conducted on a digital phantom and a physical phantom demonstrate a superior performance of our method over other methods in terms of image quality and computational efficiency, which validates its potential for low-dose CT imaging.展开更多
Sparse representation is a mathematical model for data representation that has proved to be a powerful tool for solving problems in various fields such as pattern recognition, machine learning, and computer vision. As...Sparse representation is a mathematical model for data representation that has proved to be a powerful tool for solving problems in various fields such as pattern recognition, machine learning, and computer vision. As one of the building blocks of the sparse representation method, dictionary learning plays an important role in the minimization of the reconstruction error between the original signal and its sparse representation in the space of the learned dictionary. Although using training samples directly as dictionary bases can achieve good performance, the main drawback of this method is that it may result in a very large and inef- ficient dictionary due to noisy training instances. To obtain a smaller and more representative dictionary, in this paper, we propose an approach called Laplacian sparse dictionary (LSD) learning. Our method is based on manifold learning and double sparsity. We incorporate the Laplacian weighted graph in the sparse representation model and impose the 11-norm sparsity on the dictionary. An LSD is a sparse overcomplete dictionary that can preserve the intrinsic structure of the data and learn a smaller dictionary for each class. The learned LSD can be easily integrated into a classification framework based on sparse representation. We compare the proposed method with other methods using three benchmark-controlled face image databases, Extended Yale B, ORL, and AR, and one uncontrolled person image dataset, i-LIDS-MA. Results show the advantages of the proposed LSD algorithm over state-of-the-art sparse representation based classification methods.展开更多
基金jointly supported by the National Science and Technology Major Project(Nos.2016ZX05002-005-07HZ,2016ZX05014-001-008HZ,and 2016ZX05026-002-002HZ)National Natural Science Foundation of China(Nos.41720104006 and 41274124)+2 种基金Chinese Academy of Sciences Strategic Pilot Technology Special Project(A)(No.XDA14010303)Shandong Province Innovation Project(No.2017CXGC1602)Independent Innovation(No.17CX05011)。
文摘Full waveform inversion(FWI)is an extremely important velocity-model-building method.However,it involves a large amount of calculation,which hindsers its practical application.The multi-source technology can reduce the number of forward modeling shots during the inversion process,thereby improving the efficiency.However,it introduces crossnoise problems.In this paper,we propose a sparse constrained encoding multi-source FWI method based on K-SVD dictionary learning.The phase encoding technology is introduced to reduce crosstalk noise,whereas the K-SVD dictionary learning method is used to obtain the basis of the transformation according to the characteristics of the inversion results.The multiscale inversion method is adopted to further enhance the stability of FWI.Finally,the synthetic subsag model and the Marmousi model are set to test the effectiveness of the newly proposed method.Analysis of the results suggest the following:(1)The new method can effectively reduce the computational complexity of FWI while ensuring inversion accuracy and stability;(2)The proposed method can be combined with the time-domain multi-scale FWI strategy flexibly to further avoid the local minimum and to improve the stability of inversion,which is of significant importance for the inversion of the complex model.
基金financially supported the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA14020102)the National Natural Science Foundation of China (Nos. 41774125,41530320 and 41804098)the Key National Research Project of China (Nos. 2016YFC0303100,2017YFC0601900)。
文摘Time-domain airborne electromagnetic(AEM)data are frequently subject to interference from various types of noise,which can reduce the data quality and affect data inversion and interpretation.Traditional denoising methods primarily deal with data directly,without analyzing the data in detail;thus,the results are not always satisfactory.In this paper,we propose a method based on dictionary learning for EM data denoising.This method uses dictionary learning to perform feature analysis and to extract and reconstruct the true signal.In the process of dictionary learning,the random noise is fi ltered out as residuals.To verify the eff ectiveness of this dictionary learning approach for denoising,we use a fi xed overcomplete discrete cosine transform(ODCT)dictionary algorithm,the method-of-optimal-directions(MOD)dictionary learning algorithm,and the K-singular value decomposition(K-SVD)dictionary learning algorithm to denoise decay curves at single points and to denoise profi le data for diff erent time channels in time-domain AEM.The results show obvious diff erences among the three dictionaries for denoising AEM data,with the K-SVD dictionary achieving the best performance.
文摘The bronze gui food container published in 2007 bears an inscription that records the event of the Zhou king enfeoffing earl Tang in the Jin State and granting him the title "marquis." But the vessel owner "■ 公" (Duke ■) has not been identified all along. As the author believes,this character should be taken as" (爻疋) (疏)"seen in the Shuo Wen《说文》because it contains the component "爻"and is read "疋"(夏).
基金Project(NCET-11-0866)supported by Education Ministry's new Century Excellent Talents Supporting Plan,China
文摘Parameter estimation of the attributed scattering center(ASC) model is significant for automatic target recognition(ATR). Sparse representation based parameter estimation methods have developed rapidly. Construction of the separable dictionary is a key issue for sparse representation technology. A compressive time-domain dictionary(TD) for ASC model is presented. Two-dimensional frequency domain responses of the ASC are produced and transformed into the time domain. Then these time domain responses are cutoff and stacked into vectors. These vectored time-domain responses are amalgamated to form the TD. Compared with the traditional frequency-domain dictionary(FD), the TD is a matrix that is quite spare and can markedly reduce the data size of the dictionary. Based on the basic TD construction method, we present four extended TD construction methods, which are available for different applications. In the experiments, the performance of the TD, including the basic model and the extended models, has been firstly analyzed in comparison with the FD. Secondly, an example of parameter estimation from SAR synthetic aperture radar(SAR) measurements of a target collected in an anechoic room is exhibited. Finally, a sparse image reconstruction example is from two apart apertures. Experimental results demonstrate the effectiveness and efficiency of the proposed TD.
基金Supported by the National Natural Science Foundation of China under Grants (No. 60872123 & U0835001)by Natural Science Foundation of Guangdong Province, China (No. 07006496)
文摘The K-COD (K-Complete Orthogonal Decomposition) algorithm for generating adaptive dictionary for signals sparse representation in the framework of K-means clustering is proposed in this paper,in which rank one approximation for components assembling signals based on COD and K-means clustering based on chaotic random search are well utilized. The results of synthetic test and empirical experiment for the real data show that the proposed algorithm outperforms recently reported alternatives: K-Singular Value Decomposition (K-SVD) algorithm and Method of Optimal Directions (MOD) algorithm.
基金Project supported by the National High-Tech R&D Program (863) of China (No. 2015AA016704e) and the Zhejiang Provincial Natural Science Foundation, China (No. LY14F020028)
文摘Recently, low-dose computed tomography (CT) has become highly desirable because of the growing concern for the potential risks of excessive radiation. For low-dose CT imaging, it is a significant challenge to guarantee image quality while reducing radiation dosage. Compared with classical filtered backprojection algorithms, compressed sensing-based iterative re- construction has achieved excellent imaging performance, but its clinical application is hindered due to its computational ineffi- ciency. To promote low-dose CT imaging, we propose a promising reconstruction scheme which combines total-variation mini- mization and sparse dictionary learning to enhance the reconstruction performance, and properly schedule them with an adaptive iteration stopping strategy to boost the reconstruction speed. Experiments conducted on a digital phantom and a physical phantom demonstrate a superior performance of our method over other methods in terms of image quality and computational efficiency, which validates its potential for low-dose CT imaging.
基金Project supported by the National Natural Science Foundation of China (Nos. 61272304 and 61363029) and the Guangxi Key Laboratory of Trusted Software (No. kx201313)
文摘Sparse representation is a mathematical model for data representation that has proved to be a powerful tool for solving problems in various fields such as pattern recognition, machine learning, and computer vision. As one of the building blocks of the sparse representation method, dictionary learning plays an important role in the minimization of the reconstruction error between the original signal and its sparse representation in the space of the learned dictionary. Although using training samples directly as dictionary bases can achieve good performance, the main drawback of this method is that it may result in a very large and inef- ficient dictionary due to noisy training instances. To obtain a smaller and more representative dictionary, in this paper, we propose an approach called Laplacian sparse dictionary (LSD) learning. Our method is based on manifold learning and double sparsity. We incorporate the Laplacian weighted graph in the sparse representation model and impose the 11-norm sparsity on the dictionary. An LSD is a sparse overcomplete dictionary that can preserve the intrinsic structure of the data and learn a smaller dictionary for each class. The learned LSD can be easily integrated into a classification framework based on sparse representation. We compare the proposed method with other methods using three benchmark-controlled face image databases, Extended Yale B, ORL, and AR, and one uncontrolled person image dataset, i-LIDS-MA. Results show the advantages of the proposed LSD algorithm over state-of-the-art sparse representation based classification methods.