In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection,an improved detectio...In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection,an improved detection algorithm of infrared small and dim target is proposed in this paper.Firstly,the original infrared images are changed into a new infrared patch tensor mode through data reconstruction.Then,the infrared small and dim target detection problems are converted to low-rank tensor recovery problems based on tensor nuclear norm in accordance with patch tensor characteristics,and inverse variance weighted entropy is defined for self-adaptive adjustment of sparseness.Finally,the low-rank tensor recovery problem with noise is solved by alternating the direction method to obtain the sparse target image,and the final small target is worked out by a simple partitioning algorithm.The test results in various spacebased downward-looking complex scenes show that such method can restrain complex background well by virtue of rapid arithmetic speed with high detection probability and low false alarm rate.It is a kind of infrared small and dim target detection method with good performance.展开更多
现有多视角聚类算法存在:1)在学习低维表征的过程中无法准确捕获或忽略嵌入在多视角数据中的高阶信息和互补信息;2)未能准确捕获数据局部信息;3)信息捕获方法缺少对噪声点鲁棒性等问题.为解决上述问题,提出一种自适应张量奇异值收缩的...现有多视角聚类算法存在:1)在学习低维表征的过程中无法准确捕获或忽略嵌入在多视角数据中的高阶信息和互补信息;2)未能准确捕获数据局部信息;3)信息捕获方法缺少对噪声点鲁棒性等问题.为解决上述问题,提出一种自适应张量奇异值收缩的多视角聚类(multi-view clustering based on adaptive tensor singular value shrinkage,ATSVS)算法.ATSVS首先提出一种符合秩特性的张量对数行列式函数对表示张量施加低秩约束,在张量奇异值分解(tensor singular value decomposition,t-SVD)过程中能够根据奇异值自身大小进行自适应收缩,更加准确地进行张量秩估计,进而从全局角度精准捕获多视角数据的高阶信息和互补信息.然后采用一种结合稀疏表示和流形正则技术优势的l_(1,2)范数捕获数据的局部信息,并结合l_(2,1)范数对噪声施加稀疏约束,提升算法对噪声点的鲁棒性.与11个对比算法在9个数据集上的实验结果显示,ATSVS的聚类性能均优于其他对比算法.因此,ATSVS是一个能够有效处理多视角数据聚类任务的优秀算法.展开更多
In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice ...In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice matrix under unfold operator,and then the fold operator is used to form the next iteration tensor such that the computing time can be decreased.In theory,we analyze the global convergence of the algorithm.In numerical experiment,the simulation data and real image inpainting are carried out.Experiment results show the parallel algorithm outperform its original algorithm in CPU times under the same precision.展开更多
文摘In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection,an improved detection algorithm of infrared small and dim target is proposed in this paper.Firstly,the original infrared images are changed into a new infrared patch tensor mode through data reconstruction.Then,the infrared small and dim target detection problems are converted to low-rank tensor recovery problems based on tensor nuclear norm in accordance with patch tensor characteristics,and inverse variance weighted entropy is defined for self-adaptive adjustment of sparseness.Finally,the low-rank tensor recovery problem with noise is solved by alternating the direction method to obtain the sparse target image,and the final small target is worked out by a simple partitioning algorithm.The test results in various spacebased downward-looking complex scenes show that such method can restrain complex background well by virtue of rapid arithmetic speed with high detection probability and low false alarm rate.It is a kind of infrared small and dim target detection method with good performance.
文摘现有多视角聚类算法存在:1)在学习低维表征的过程中无法准确捕获或忽略嵌入在多视角数据中的高阶信息和互补信息;2)未能准确捕获数据局部信息;3)信息捕获方法缺少对噪声点鲁棒性等问题.为解决上述问题,提出一种自适应张量奇异值收缩的多视角聚类(multi-view clustering based on adaptive tensor singular value shrinkage,ATSVS)算法.ATSVS首先提出一种符合秩特性的张量对数行列式函数对表示张量施加低秩约束,在张量奇异值分解(tensor singular value decomposition,t-SVD)过程中能够根据奇异值自身大小进行自适应收缩,更加准确地进行张量秩估计,进而从全局角度精准捕获多视角数据的高阶信息和互补信息.然后采用一种结合稀疏表示和流形正则技术优势的l_(1,2)范数捕获数据的局部信息,并结合l_(2,1)范数对噪声施加稀疏约束,提升算法对噪声点的鲁棒性.与11个对比算法在9个数据集上的实验结果显示,ATSVS的聚类性能均优于其他对比算法.因此,ATSVS是一个能够有效处理多视角数据聚类任务的优秀算法.
基金Supported by National Nature Science Foundation(12371381)Nature Science Foundation of Shanxi(202403021222270)。
文摘In this paper,we established a class of parallel algorithm for solving low-rank tensor completion problem.The main idea is that N singular value decompositions are implemented in N different processors for each slice matrix under unfold operator,and then the fold operator is used to form the next iteration tensor such that the computing time can be decreased.In theory,we analyze the global convergence of the algorithm.In numerical experiment,the simulation data and real image inpainting are carried out.Experiment results show the parallel algorithm outperform its original algorithm in CPU times under the same precision.
