摘要
Sparsity Adaptive Matching Pursuit (SAMP) algorithm is a widely used reconstruction algorithm for compressive sensing in the case that the sparsity is unknown. In order to match the sparsity more accurately, we presented an improved SAMP algorithm based on Regularized Backtracking (SAMP-RB). By adapting a regularized backtracking step to SAMP algorithm in each iteration stage, the proposed algorithm can flexibly remove the inappropriate atoms. The experimental results show that SAMP-RB reconstruction algorithm greatly improves SAMP algorithm both in reconstruction quality and computational time. It has better reconstruction efficiency than most of the available matching pursuit algorithms.
Sparsity Adaptive Matching Pursuit （SAMP） algorithm is a widely used reconstruction algorithm for compressive sensing in the case that the sparsity is unknown. In order to match the sparsity more accurately, we presented an improved SAMP algorithm based on Regularized Back- tracking （SAMP-RB）. By adapting a regularized backtracking step to SAMP algorithm in each it- eration stage, the proposed algorithm can flexibly remove the inappropriate atoms. The experimental results show that SAMP-RB reconstruction algorithm greatly improves SAMP algorithm both in re- construction quality and computational time. It has better reconstruction efficiency than most of the available matching pursuit algorithms.
基金
Supported by the National Natural Science Foundation of China (No. 61073079)
the Fundamental Research Funds for the Central Universities (2011JBM216,2011YJS021)
