Regularized Adaptive Matching Pursuit Algorithm for Signal Reconstruction Based on Compressive Sensing
Liu Ya-xin Zhao Rui-zhen Hu Shao-hai Jiang Chun-hui
(Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China)
(Key Laboratory of Advanced Information Science and Network Technology of Beijing, Beijing 100044, China)
Abstract：Compressive sensing is a novel signal sampling theory under the condition that the signal is sparse or compressible. In this case, the small amount of signal values can be reconstructed accurately when the signal is sparse or compressible. In this paper, a new Regularized Adaptive Matching Pursuit (RAMP) algorithm is presented with the idea of regularization. The proposed algorithm could control the accuracy of reconstruction by both the adaptive process which chooses the candidate set automatically and the regularization process which gets the atoms in the final support set although the sparsity of the original signal is unknown. The experimental results show that the proposed algorithm can get better reconstruction performances and it is superior to other algorithms both visually and objectively.
2010, Vol. 32 
