scholarly journals Reduced Computational Complexity Orthogonal Matching Pursuit Using a Novel Partitioned Inversion Technique for Compressive Sensing

Electronics ◽  
2018 ◽  
Vol 7 (9) ◽  
pp. 206 ◽  
Author(s):  
Seonggeon Kim ◽  
Uihyun Yun ◽  
Jaehyuk Jang ◽  
Geunsu Seo ◽  
Jongjin Kang ◽  
...  
Keyword(s):  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Orthogonal Matching Pursuit ◽  
Inversion Method ◽  
Fpga Design ◽  
The Matrix ◽  
Field Programmable ◽  
Previous Iteration ◽  
Reconstruction Time

This paper reports a field-programmable gate array (FPGA) design of compressed sensing (CS) using the orthogonal matching pursuit (OMP) algorithm. While solving the least-squares (LS) problem in the OMP algorithm, the complexity of the matrix inversion operation at every loop is reduced by the proposed partitioned inversion that utilizes the inversion result in the previous iteration. By the proposed matrix (n × n) inversion method inside the OMP, the number of operations is reduced down from O(n3) to O(n2). The OMP algorithm is implemented with a Xilinx Kintex UltraScale. The architecture with the proposed partitioned inversion involves 722 less DSP48E compared with the conventional method. It operates with a sample period of 4 ns, signal reconstruction time of 27 μs, and peak signal to noise ratio (PSNR) of 30.26 dB.

scholarly journals Wavelet Compressive Sampling Signal Reconstruction Using Upside-Down Tree Structure

2011 ◽  
Vol 2011 ◽  
pp. 1-10
Author(s):  
Yijiu Zhao ◽  
Xiaoyan Zhuang ◽  
Zhijian Dai ◽  
Houjun Wang
Keyword(s):  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Compressive Sampling ◽  
Tree Structure ◽  
Orthogonal Matching Pursuit ◽  
Reconstruction Method ◽  
Wavelet Domain ◽  
Wavelet Coefficients ◽  
Signal To Noise

This paper suggests an upside-down tree-based orthogonal matching pursuit (UDT-OMP) compressive sampling signal reconstruction method in wavelet domain. An upside-down tree for the wavelet coefficients of signal is constructed, and an improved version of orthogonal matching pursuit is presented. The proposed algorithm reconstructs compressive sampling signal by exploiting the upside-down tree structure of the wavelet coefficients of signal besides its sparsity in wavelet basis. Compared with conventional greedy pursuit algorithms: orthogonal matching pursuit (OMP) and tree-based orthogonal matching pursuit (TOMP), signal-to-noise ratio (SNR) using UDT-OMP is significantly improved.

scholarly journals A Matching Pursuit Algorithm for Backtracking Regularization Based on Energy Sorting

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 231 ◽  
Author(s):  
Hanfei Zhang ◽  
Shungen Xiao ◽  
Ping Zhou
Keyword(s):  
Energy Distribution ◽  
Matching Pursuit ◽  
Signal Reconstruction ◽  
Selection Criterion ◽  
Critical Factor ◽  
Maximum Energy ◽  
Orthogonal Matching Pursuit ◽  
Support Set ◽  
Reconstruction Efficiency ◽  
Matching Pursuit Algorithm

The signal reconstruction quality has become a critical factor in compressed sensing at present. This paper proposes a matching pursuit algorithm for backtracking regularization based on energy sorting. This algorithm uses energy sorting for secondary atom screening to delete individual wrong atoms through the regularized orthogonal matching pursuit (ROMP) algorithm backtracking. The support set is continuously updated and expanded during each iteration. While the signal energy distribution is not uniform, or the energy distribution is in an extreme state, the reconstructive performance of the ROMP algorithm becomes unstable if the maximum energy is still taken as the selection criterion. The proposed method for the regularized orthogonal matching pursuit algorithm can be adopted to improve those drawbacks in signal reconstruction due to its high reconstruction efficiency. The experimental results show that the algorithm has a proper reconstruction.

Signal Reconstruction Based on a Fusion Compressed Sensing Frame

2013 ◽  
Vol 785-786 ◽  
pp. 1315-1323
Author(s):  
Xu Hua Li ◽  
Yue Li Chen ◽  
Nan Jun Hu ◽  
Wei Li ◽  
Tian Jun Yuan ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Greedy Algorithms ◽  
Reconstruction Error ◽  
Reconstruction Accuracy ◽  
Support Sets ◽  
Reconstruction Effects ◽  
Better Than

Greedy algorithms represented by orthogonal matching pursuit (OMP) and subspace pursuit (SP) algorithms are practically used in image processing based upon compressed sensing theory. However, there are two disadvantages: 1)Relatively poor signal reconstruction accuracy; 2) High computation complexity and measurements time. This paper proposes a frame of greedy algorithms obtaining a novel fusion of matching pursuit (FMP), combining the OMP and SP algorithms. FMP unites the two support sets from OMP and SP selecting the most appropriate atoms to achieve secondary screening of the original two support sets, finally realizing the accurate signal reconstruction. Using same test conditions, image reconstruction experiments and stability of Frame, the proposed FMP algorithm can effectively improve signal-to-noise ratio (SNR) with improved reconstruction error. Reconstruction effects using proposed FMP are better than separately using other two greedy algorithms for both high and low resolution images.

scholarly journals Tree-Based Backtracking Orthogonal Matching Pursuit for Sparse Signal Reconstruction

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yigang Cen ◽  
Fangfei Wang ◽  
Ruizhen Zhao ◽  
Lihong Cui ◽  
Lihui Cen ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Optimization Problem ◽  
Selection Process ◽  
Matching Pursuit ◽  
Signal Reconstruction ◽  
Search Space ◽  
Orthogonal Matching Pursuit ◽  
Superior Performance ◽  
Sparse Signal ◽  
Tree Model

Compressed sensing (CS) is a theory which exploits the sparsity characteristic of the original signal in signal sampling and coding. By solving an optimization problem, the original sparse signal can be reconstructed accurately. In this paper, a new Tree-based Backtracking Orthogonal Matching Pursuit (TBOMP) algorithm is presented with the idea of the tree model in wavelet domain. The algorithm can convert the wavelet tree structure to the corresponding relations of candidate atoms without any prior information of signal sparsity. Thus, the atom selection process will be more structural and the search space can be narrowed. Moreover, according to the backtracking process, the previous chosen atoms’ reliability can be detected and the unreliable atoms can be deleted at each iteration, which leads to an accurate reconstruction of the signal ultimately. Compared with other compressed sensing algorithms, simulation results show the proposed algorithm’s superior performance to that of several other OMP-type algorithms.

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