1(College of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China）2（Guizhou Key Laboratory of Public Big Data,Guiyang 550025,China）3（State Grid Chongqing Electric Power Company,Chongqing 400014,China)
Abstract：Since compressed sensing theory is proposed,the algorithm of image reconstruction plays an irreplaceable role in CS and arouses researchers′ wide concern.A Sparsity Adaptive Compressive Sampling Matching Pursuit algorithm is proposed in order to tackle unknown sparsity of current greedy algorithms in compression sampling.And meanwhile,the performance of image reconstruction algorithm can be evaluated by making use of Peak Signal-to-Noise Ratio and Reconstruction Error Possibility.The simulation results indicate that the proposed algorithm has the following advantages of strong adaptability,high accuracy and amazing image reconstruction effects under meeting the condition of Restricted Isometry Property.
2017, Vol. 38 
