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Compressed Sensing Image Sequence Reconstruction Algorithm Based on Sparse Support Prior


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Compressed Sensing Image Sequence Reconstruction Algorithm Based on Sparse Support Prior
LI Xingxiu1WEI Zhihui2XIAO Liang2
1.School of Sciences;2.School of Computer Science and Engineering,NUST,Nanjing 210094,China
sparse supportcompressed sensingimage sequencesresidual compensation
Aiming at the problems of low accuracy and more model parameters of traditional compressed sensing image sequence reconstruction algorithms,a novel algorithm combining sparse support prior and residual compensation is proposed.The initial estimation of the current image is obtained by solving a weighted l1 norm minimization problem based on knowing the reconstruction of the previous image.The final estimation of the current image is generated by the compressed sensing reconstruction of the estimation error and the compensation of the original estimation.Compared with other similar algorithms,the proposed algorithm reduces the number of threshold parameters.Experimental results show that the proposed algorithm is superior to other similar algorithms in terms of relative error,peak signal to noise radio and structural similarity of reconstructed images with same number of measured values.


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Last Update: 2012-12-29