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Fuzzy adaptive method for optimizing recovery of compressive sensing matrix


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Fuzzy adaptive method for optimizing recovery of compressive sensing matrix
Song Xiaoning123Xue Yishi4Yu Dongjun1Yang Xibei123Liu Zi12
1.School of Computer Science and Engineering,NUST,Nanjing 210094,China; 2.Post-doctoral Research Center,Jiangsu Sunboon Information Technology Co.,Ltd.,Wuxi 214072,China; 3.School of Computer Science and Engineering,Jiangsu University of Science and Technology, Zhenjiang 212003,China; 4.Overseas Education College,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
sparse representation sensing matrix compressive measurement optimization recovery image recognition
To solve the underdetermined linear problem in the signal recovery from high-dimensional data,a fuzzy adaptive method for optimizing recovery of compressive sensing matrix is proposed for image reconstruction and recognition.By this means,each high dimensional input sample is firstly partitioned into the several local blocks,and those local blocks are combined to represent the sample as a third-order tensor.Moreover,the compressive measurement is performed on the dimensionality-reduced source signal,which is able to find the properties of statistical independence and linear singular by using multi-scale structural analysis and independent component analysis.Finally,a new fuzzy cost function for optimization of sensing matrix is proposed,in which the update of atoms from sensing matrix are fuzzily handled,and the low coherence is obtained between the properties of observation matrix and dictionary matrix.The merit of the method is that the sparse signal has desirable properties for the number of measurements and representation qualities under the same reconstruction conditions.Extensive experimental studies conducted on ORL,Yale face images and 91 natural images databases show that the effectiveness of the proposed method.


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Last Update: 2013-08-31