针对强海杂波背景下微弱动目标信号提取困难、雷达检测性能差的问题,在稀疏表示理论的基础上,提出利用字典学习算〖JP2〗法抑制海杂波、重构目标信号。该算法通过K类奇异值分解(K-singular value decomposition,K-SVD)算法学习海杂波和...针对强海杂波背景下微弱动目标信号提取困难、雷达检测性能差的问题,在稀疏表示理论的基础上,提出利用字典学习算〖JP2〗法抑制海杂波、重构目标信号。该算法通过K类奇异值分解(K-singular value decomposition,K-SVD)算法学习海杂波和目标的稀疏域主成分特征,得到相应的学习字典,抑制海杂波并对目标信号稀疏重建,解决了以往固定字典与高海况下雷达回波匹配度低、目标信号提取效果差的问题;并通过算法参数的分析和优化,进一步提高了算法性能和工程实用性。基于实测数据的实验结果表明,相比传统检测方法,所提算法能够有效检测高海况下微弱动目标,显著提升检测性能。展开更多
为了解决中度和重度污染的模糊图像去模糊效果差、边缘细节恢复难的问题,提出一种基于压缩感知理论的去模糊算法,即引入数字水印中的Zig-Zag变换,加入信号转换过程中的位置关系信息,对K-奇异值分解(K-singular value decomposition,K-S...为了解决中度和重度污染的模糊图像去模糊效果差、边缘细节恢复难的问题,提出一种基于压缩感知理论的去模糊算法,即引入数字水印中的Zig-Zag变换,加入信号转换过程中的位置关系信息,对K-奇异值分解(K-singular value decomposition,K-SVD)算法进行改进,以优化稀疏系数的稀疏度。实验结果表明,对于中重度模糊图像,新算法的提高信噪比(Improve Signal to Noise Ratio,ISNR)优于K-SVD算法。展开更多
针对传统方法进行岩心图像压缩感知重构时,在低码率下容易产生细节丢失的问题,提出一种基于K-SVD(K-Singular Value Decomposition)超完备字典学习的压缩感知重构算法。首先根据分块压缩感知理论,将岩心图像分块,采用高斯随机矩阵对相...针对传统方法进行岩心图像压缩感知重构时,在低码率下容易产生细节丢失的问题,提出一种基于K-SVD(K-Singular Value Decomposition)超完备字典学习的压缩感知重构算法。首先根据分块压缩感知理论,将岩心图像分块,采用高斯随机矩阵对相应层级的图像块进行观测,得到对应的观测值块,然后用MMSE(Minimum Mean Squareerror Estimation)方法获得初始解的估计并利用提示小波进行滤波,通过全局阈值的思想得到自适应阈值,最后利用K-SVD字典结合Landweber迭代实现压缩与重构。实验结果表明,与传统方法相比,在相同的采样率下获得的重构图像能较好地保留岩心图像的纹理信息,重构岩心图像的PSNR(Peak Signal to Noise Ratio)值提高约0. 1~0. 8 dB。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
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
文摘针对强海杂波背景下微弱动目标信号提取困难、雷达检测性能差的问题,在稀疏表示理论的基础上,提出利用字典学习算〖JP2〗法抑制海杂波、重构目标信号。该算法通过K类奇异值分解(K-singular value decomposition,K-SVD)算法学习海杂波和目标的稀疏域主成分特征,得到相应的学习字典,抑制海杂波并对目标信号稀疏重建,解决了以往固定字典与高海况下雷达回波匹配度低、目标信号提取效果差的问题;并通过算法参数的分析和优化,进一步提高了算法性能和工程实用性。基于实测数据的实验结果表明,相比传统检测方法,所提算法能够有效检测高海况下微弱动目标,显著提升检测性能。
文摘为了解决中度和重度污染的模糊图像去模糊效果差、边缘细节恢复难的问题,提出一种基于压缩感知理论的去模糊算法,即引入数字水印中的Zig-Zag变换,加入信号转换过程中的位置关系信息,对K-奇异值分解(K-singular value decomposition,K-SVD)算法进行改进,以优化稀疏系数的稀疏度。实验结果表明,对于中重度模糊图像,新算法的提高信噪比(Improve Signal to Noise Ratio,ISNR)优于K-SVD算法。
文摘针对传统方法进行岩心图像压缩感知重构时,在低码率下容易产生细节丢失的问题,提出一种基于K-SVD(K-Singular Value Decomposition)超完备字典学习的压缩感知重构算法。首先根据分块压缩感知理论,将岩心图像分块,采用高斯随机矩阵对相应层级的图像块进行观测,得到对应的观测值块,然后用MMSE(Minimum Mean Squareerror Estimation)方法获得初始解的估计并利用提示小波进行滤波,通过全局阈值的思想得到自适应阈值,最后利用K-SVD字典结合Landweber迭代实现压缩与重构。实验结果表明,与传统方法相比,在相同的采样率下获得的重构图像能较好地保留岩心图像的纹理信息,重构岩心图像的PSNR(Peak Signal to Noise Ratio)值提高约0. 1~0. 8 dB。
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.
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.
