基于MSSOR求解信号恢复问题的ADMM算法
ADMM Algorithm for Solving Signal Recovery Problem Based on MSSOR
摘要: 基于改进的对称逐次超松弛(MSSOR)方法,本文针对信号恢复问题提出了一种交替方向乘子(ADMM)法。该方法是一种内外部迭代相结合的方法,其中内部迭代为MSSOR方法,外部迭代为ADMM方法。在适当条件下,证明了所提算法的全局收敛性,数值结果表明,该方法既能在较短的时间内恢复信号,又能提高重构图像的质量。
Abstract: Based on the improved symmetric successive overrelaxation (MSSOR) method, an alternating direction multiplier (ADMM) method is proposed to solve the signal recovery problem. The method is a combination of internal and external iterations, in which the internal iteration is MSSOR method and the external iteration is ADMM method. Under appropriate conditions, the global convergence of the proposed algorithm is proved. Numerical results show that the proposed method can not only restore the signal in a short time, but also improve the quality of the reconstructed image.
文章引用:袁月, 宇振盛. 基于MSSOR求解信号恢复问题的ADMM算法[J]. 应用数学进展, 2021, 10(11): 3932-3941. https://doi.org/10.12677/AAM.2021.1011418

参考文献

[1] Liu, H. and Peng, J. (2018) Sparse Signal Recovery via Alternating Projection Method. Signal Processing, 143, 161-170. [Google Scholar] [CrossRef
[2] Ambat, S.K. and Hari, K.V.S. (2015) An Iterative Framework for Sparse Signal Reconstruction Algorithms. Signal Processing, 108, 351-364. [Google Scholar] [CrossRef
[3] Zhang, H., Dong, Y. and Fan, Q. (2017) Wavelet Frame Based Poisson Noiseremoval and Image Deblurring. Signal Processing, 137, 363-372. [Google Scholar] [CrossRef
[4] Chen, C., Tramel, E.W. and Fowler, J.E. (2011) Compressed-Sensing Recovery of Images and Video Using Multihypothesis Predictions. Proceedings of the 45th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, 6-9 November 2011, 1193-1198. [Google Scholar] [CrossRef
[5] Zhang, J., Xiang, Q., Yin, Y., et al. (2017) Adaptive Compressed Sensing for Wireless Image Sensor Networks. Multimedia Tools and Applications, 76, 4227-4242. [Google Scholar] [CrossRef
[6] Kumar, A. (2017) Deblurring of Motion Blurred Images Using Histogram of Oriented Gradients and Geometric Moments. Signal Processing: Image Communication, 55, 55-65. [Google Scholar] [CrossRef
[7] D’Acunto, M., Benassi, A., Moroni, D., et al. (2016) 3D Image Reconstruction Using Radon Transform. SIViP, 10, 1-8. [Google Scholar] [CrossRef
[8] Zhang, X.M. and Han, Q.L. (2013) Network-Based H∞ Filtering Using a Logic Jumping-Like Trigger. Automatica, 49, 1428-1435. [Google Scholar] [CrossRef
[9] Bruckstein, A.M., Donoho, D.L. and Elad, M. (2009) From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images. SIAM Review, 51, 34-81. [Google Scholar] [CrossRef
[10] Donoho, D.L. (2006) Compressed Sensing. IEEE Transactions on Information Theory, 52, 1289-1306. [Google Scholar] [CrossRef
[11] Donoho, D.L. (2006) For Most Large Underdetermined Systems of Linear Equations Theminimal l1 Norm Solution Is Also the Sparsest Solution. Communications on Pure and Applied Mathematics, 59, 797-829. [Google Scholar] [CrossRef
[12] Daubechies, I., Defrise, M. and De Mol, C. (2004) An Iterative Thresholding Algorithm for Linear Inverse Problems with a Sparsity Constraint. Communications on Pure and Applied Mathematics, 57, 1413-1457. [Google Scholar] [CrossRef
[13] Beck, A. and Teboulle, M. (2009) A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems. SIAM Journal on Imaging Sciences, 2, 183-202. [Google Scholar] [CrossRef
[14] Hale, E.T., Yin, W. and Zhang, Y. (2007) A Fixed-Point Continuation Method for 1 Regularized Minimization with Applications to Compressed Sensing. CAAM TR07-07, Vol. 43, Rice University, Houston, 44.
[15] Huang, S. and Wan, Z. (2017) A New Nonmonotone Spectral Residual Method for Nonsmooth Nonlinear Equations. Journal of Computational and Applied Mathematics, 313, 82-101. [Google Scholar] [CrossRef
[16] Figueiredo, M.A.T., Nowak, R.D. and Wright, S.J. (2007) Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems. IEEE Journal of Selected Topics in Signal Processing, 1, 586-597. [Google Scholar] [CrossRef
[17] Xiao, Y. and Zhu, H. (2013) A Conjugate Gradient Method to Solve Convex Constrained Monotone Equations with Applications in Compressive Sensing. Journal of Mathematical Analysis and Applications, 405, 310-319. [Google Scholar] [CrossRef
[18] Xiao, Y., Wang, Q. and Hu, Q. (2011) Nonsmooth Equations Based Method for l1 Norm Problems with Applications to Compressed Sensing. Nonlinear Analysis, Theory, Methods and Applications, 74, 3570-3577. [Google Scholar] [CrossRef
[19] Wan, Z., Liu, W.Y. and Wang, C. (2016) An Improved Projection Based Derivative-Free Algorithm for Solving Nonlinear Monotone Symmetric Equations. Pacific Journal of Optimization, 12, 603-622.
[20] Ouaddah, A. and Boughaci, D. (2016) Harmony Search Algorithm for Image Reconstruction from Projections. Applied Soft Computing, 46, 924-935. [Google Scholar] [CrossRef
[21] Bahaoui, Z., El Fadili, H., Zenkouar, K., et al. (2017) Exact Zernike and Pseudo-Zernike Moments Image Reconstruction Based on Circular Overlapping Blocks and Chamfer Distance. SIViP, 11, 1-8. [Google Scholar] [CrossRef
[22] Hestenes, M.R. (1969) Multiplier and Gradient Methods. Journal of Optimization Theory and Application, 4, 303-320. [Google Scholar] [CrossRef
[23] Glowinski, R. (1984) Numerical Methods for Nonlinear Variational Problems. Springer-Verlag, New York. [Google Scholar] [CrossRef
[24] Gabay, D. and Mercier, B. (1976) A Dual Algorithm for the Solution of Nonlinear Variational Problems via Finite Element Approximations. Computers & Mathematics with Applications, 2, 17-40. [Google Scholar] [CrossRef
[25] Hestenes, M.R. (1969) Multiplier and Gradient Methods. Journal of Optimization Theory and Applications, 4, 303-320. [Google Scholar] [CrossRef
[26] Zhang, J.-J. (2015) MSSOR-Based Alternating Direction Method for Symmetric Positive-Definite Linear Complementarity Problems. Numerical Algorithms, 68, 631-644. [Google Scholar] [CrossRef
[27] Bai, Z.-Z. (1999) A Class of Modified Block SSOR Preconditioners for Symmetric Positive Definite Systems of Linear Equations. Advances in Computational Mathematics, 10, 169-186.
[28] Bai, Z.-Z. (2001) Modified Block SSOR Preconditioners for Symmetric Positive Definite Linear Systems. Annals of Operations Research, 103, 263-282.
[29] Murty, K.G. (1997) Linear Complementarity, Linear and Nonlinear Programming.
[30] Wan, Z., Guo, J., Liu, J., et al. (2018) A Modified Spectral Conjugate Gradient Projection Method for Signal Recovery. SIViP, 12, 1455-1462. [Google Scholar] [CrossRef
[31] Bai, Z.-Z. (2010) Modulus-Based Matrix Splitting Iteration Methods for Linear Complementarity Problems. Numerical Linear Algebra with Applications, 6, 917-933. [Google Scholar] [CrossRef