Stagewise Weak Orthogonal Matching Pursuit Algorithm Based on Adaptive Weak Threshold and Arithmetic Mean
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초록
In the stagewise arithmetic orthogonal matching pursuit algorithm, the weak threshold used in sparsityestimation is determined via maximum iterations. Different maximum iterations correspond to differentthresholds and affect the performance of the algorithm. To solve this problem, we propose an improved variableweak threshold based on the stagewise arithmetic orthogonal matching pursuit algorithm. Our proposedalgorithm uses the residual error value to control the weak threshold. When the residual value decreases, thethreshold value continuously increases, so that the atoms contained in the atomic set are closer to the realsparsity value, making it possible to improve the reconstruction accuracy. In addition, we improved thegeneralized Jaccard coefficient in order to replace the inner product method that is used in the stagewisearithmetic orthogonal matching pursuit algorithm. Our proposed algorithm uses the covariance to replace thejoint expectation for two variables based on the generalized Jaccard coefficient. The improved generalizedJaccard coefficient can be used to generate a more accurate calculation of the correlation between themeasurement matrixes. In addition, the residual is more accurate, which can reduce the possibility of selectingthe wrong atoms. We demonstrate using simulations that the proposed algorithm produces a betterreconstruction result in the reconstruction of a one-dimensional signal and two-dimensional image signal.
Abstract
In the stagewise arithmetic orthogonal matching pursuit algorithm, the weak threshold used in sparsityestimation is determined via maximum iterations. Different maximum iterations correspond to differentthresholds and affect the performance of the algorithm. To solve this problem, we propose an improved variableweak threshold based on the stagewise arithmetic orthogonal matching pursuit algorithm. Our proposedalgorithm uses the residual error value to control the weak threshold. When the residual value decreases, thethreshold value continuously increases, so that the atoms contained in the atomic set are closer to the realsparsity value, making it possible to improve the reconstruction accuracy. In addition, we improved thegeneralized Jaccard coefficient in order to replace the inner product method that is used in the stagewisearithmetic orthogonal matching pursuit algorithm. Our proposed algorithm uses the covariance to replace thejoint expectation for two variables based on the generalized Jaccard coefficient. The improved generalizedJaccard coefficient can be used to generate a more accurate calculation of the correlation between themeasurement matrixes. In addition, the residual is more accurate, which can reduce the possibility of selectingthe wrong atoms. We demonstrate using simulations that the proposed algorithm produces a betterreconstruction result in the reconstruction of a one-dimensional signal and two-dimensional image signal.
참고문헌 (17)
- Xingyi Chen, "Block Sparse Signals Recovery Algorithm for Distributed Compressed Sensing Reconstruction", JIPS(Journal of Information Processing Systems), 15(2), pp.410~421 (2019), 한국정보처리학회[DOI]
- Y. Liu, "Design of real-time communication system for portable smart glasses based on Raspberry PI", Journal of Northeast Electric Power University, 39(4), pp.81~85 (2019)
- G. Yang, "Dagan : deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction", IEEE Transactions on Medical Imaging, 37(6), pp.1310~1321 (2018)
- C. Liu, "Design and implementation of high voltage transmission equipment auxiliary management system", Journal of Northeast Electric Power University, 39(4), pp.86~90 (2019)
- B. Li, "Phase error correction for approximated observation-based compressed sensing radar imaging", Sensors, 17(3), pp.613~ (2017)
- Rui Qi, "Block Sparse Signals Recovery via Block Backtracking- Based Matching Pursuit Method", JIPS(Journal of Information Processing Systems), 13(2), pp.360~369 (2017), 한국정보처리학회[DOI]
- Y. Liao, "A channel estimation method based on improved regularized orthogonal matching pursuit for MIMO-OFDM systems", Acta Electronica Sinica, 45(12), pp.2848~2854 (2017)
- D. Park, "Improved sufficient condition for performance guarantee in generalized orthogonal matching pursuit", IEEE Signal Processing Letters, 24(9), pp.1308~1312 (2017)
- F. Huang, "Parallel compressive sampling matching pursuit algorithm for compressed sensing signal reconstruction with OpenCL", Journal of Systems Architecture, 27, pp.51~60 (2017)
- P. Goyal, "Subspace pursuit for sparse signal reconstruction in wireless sensor networks", Procedia Computer Science, 125, pp.228~233 (2018)
- D. Lee, "MIMO OFDM channel estimation via block stagewise orthogonal matching pursuit", IEEE Communications Letters, 20(10), pp.2115~2118 (2016)
- Y. Zhang, "Stagewise arithmetic orthogonal matching pursuit", International Journal of Wireless Information Networks, 25(2), pp.221~228 (2018)
- X. Zhang, "Fast sparsity adaptive multipath matching pursuit for compressed sensing problems", Journal of Electronic Imaging, 26(3), pp.033007~ (2017)
- X. Zhang, "gOMP reconstruction algorithm based on generalized Jaccard coefficient for compressed sensing", Journal of Shandong University(Natural Science), 52(11), pp.23~28 (2017)
- Y. Mu, "A Pearson’s correlation coefficient based decision tree and its parallel implementation", Information Sciences, 435, pp.40~58 (2018)
- Zhang Leihong, "Study of Spectral Reflectance Reconstruction Based on an Algorithm for Improved Orthogonal Matching Pursuit", Current Optics and Photonics, 20(4), pp.515~523 (2016), 한국광학회
- H. Yasin, "Automated multiple related documents summarization via Jaccard’s coefficient", International Journal of Computer Applications, 13(3), pp.12~15 (2011)