A modified spectral conjugate gradient projection method for signal recovery

作者: Zhong Wan , Jie Guo , Jingjing Liu , Weiyi Liu

DOI: 10.1007/S11760-018-1300-2

关键词:

摘要: In this paper, signal recovery problems are first reformulated as a nonlinear monotone system of equations such that the modified spectral conjugate gradient projection method proposed by Wan et al. can be extended to solve problems. view equations’ analytic properties, an improved projection-based derivative-free algorithm (IPBDF) is developed. Compared with similar algorithms available in literature, advantage IPBDF search direction always sufficiently descent well being close quasi-Newton direction, without requirement computing Jacobian matrix. Then, applied into solving number test for reconstruction sparse signals and blurred images. Numerical results indicate either recover less CPU time or reconstruct images higher quality than other ones.

参考文章(27)
Matthieu Kowalski, Bruno Torrésani, Sparsity and persistence: mixed norms provide simple signal models with dependent coefficients Signal, Image and Video Processing. ,vol. 3, pp. 251- 264 ,(2009) , 10.1007/S11760-008-0076-1
Alfred M. Bruckstein, David L. Donoho, Michael Elad, From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images Siam Review. ,vol. 51, pp. 34- 81 ,(2009) , 10.1137/060657704
Li Zhang, Wei-Da Zhou, Gui-Rong Chen, Ya-Ping Lu, Fan-Zhang Li, Sparse signal reconstruction using decomposition algorithm Knowledge Based Systems. ,vol. 54, pp. 172- 179 ,(2013) , 10.1016/J.KNOSYS.2013.09.007
Yunhai Xiao, Qiuyu Wang, Qingjie Hu, Non-smooth equations based method for -norm problems with applications to compressed sensing Nonlinear Analysis: Theory, Methods & Applications. ,vol. 74, pp. 3570- 3577 ,(2011) , 10.1016/J.NA.2011.02.040
Seyed Mehdi Lajevardi, Structural similarity classifier for facial expression recognition Signal, Image and Video Processing. ,vol. 8, pp. 1103- 1110 ,(2014) , 10.1007/S11760-014-0639-2
Sooraj K. Ambat, K.V.S. Hari, An iterative framework for sparse signal reconstruction algorithms Signal Processing. ,vol. 108, pp. 351- 364 ,(2015) , 10.1016/J.SIGPRO.2014.09.023
Yunhai Xiao, Hong Zhu, A conjugate gradient method to solve convex constrained monotone equations with applications in compressive sensing Journal of Mathematical Analysis and Applications. ,vol. 405, pp. 310- 319 ,(2013) , 10.1016/J.JMAA.2013.04.017
Mario D’Acunto, Antonio Benassi, Davide Moroni, Ovidio Salvetti, 3D image reconstruction using Radon transform Signal, Image and Video Processing. ,vol. 10, pp. 1- 8 ,(2016) , 10.1007/S11760-014-0693-9
David L. Donoho, For most large underdetermined systems of linear equations the minimal 1-norm solution is also the sparsest solution Communications on Pure and Applied Mathematics. ,vol. 59, pp. 797- 829 ,(2006) , 10.1002/CPA.20132
Xian-Ming Zhang, Qing-Long Han, Network-based H∞ filtering using a logic jumping-like trigger Automatica. ,vol. 49, pp. 1428- 1435 ,(2013) , 10.1016/J.AUTOMATICA.2013.01.060