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信号重构的优化算法及其在图片恢复中的应用
An Optimization Algorithm on Signal Reconstruction and Its Application in Image Restoration

DOI: 10.12677/AAM.2023.124180, PP. 1732-1743

Keywords: 信号重建和图像去躁问题,算法,全局收敛,次线性收敛速度
The Signal Reconstruction and Image Denoising Problem
, Algorithm, Global Convergence, Sublinearly Convergent Rate

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Abstract:

本文进一步考虑信号重构与图像去躁问题的优化方法。 为此,提出了一种基于类似Armijo线搜索 的新型算法,详细证明了该算法的全局收敛性和O(1/k2)次线性收敛速率。 最后,通过稀疏信号恢 复和图像去躁的数值实验验证了所提算法的有效性和优越性。
In this paper, we further consider an optimization method for solving the signal recon- struction and image denoising problem. To this end, a new algorithm with Armijo-like line search is proposed. Global convergence results of the new algorithm is established in detail. Furthermore, we also show that the method is sublinearly convergent rate of O(1/k2). Finally, the efficiency of the proposed algorithm is illustrated through some numerical examples on sparse signal recovery and image denoising.

References

[1]  Yang, J.F. and Zhang, Y. (2011) Alternating Direction Algorithms for f1-Problems in Com- pressive Sensing. SIAM Journal on Scientific Computing, 33, 250-278.
https://doi.org/10.1137/090777761
[2]  Xiao, Y.H. and Song, H.N. (2012) An Inexact Alternating Directions Algorithm for Constrained Total Variation Regularized Compressive Sensing Problems. Journal of Mathematical Imaging and Vision, 44, 114-127.
https://doi.org/10.1007/s10851-011-0314-y
[3]  He, B.S., Ma, F. and Yuan, X.M. (2016) Convergence Study on the Symmetric Version of ADMM with Larger Step Sizes. SIAM Journal on Imaging Sciences, 9, 1467-1501.
https://doi.org/10.1137/15M1044448
[4]  Sun, M. and Liu, J. (2016) An Inexact Generalized PRSM with LQP Regularization for Struc- tured Variational Inequalities and Its Applications to Traffic Equilibrium Problems. Journal of Inequalities and Applications, 2016, Article No. 150.
https://doi.org/10.1186/s13660-016-1095-z
[5]  Sun, M., Wang, Y.J. and Liu, J. (2017) Generalized Peaceman-Rachford Splitting Method for Multiple-Block Separable Convex Programming with Applications to Robust PCA. Calcolo, 54, 77-94.
https://doi.org/10.1007/s10092-016-0177-0
[6]  Sun, M. and Liu, J. (2016) A Proximal Peaceman-Rachford Splitting Method for Compressive Sensing. Journal of Applied Mathematics and Computing, 50, 349-363.
https://doi.org/10.1007/s12190-015-0874-x
[7]  Yu, Y.C., Peng, J.G., Han, X.L. and Cui, A.A. (2017) A Primal Douglas-Rachford Splitting Method for the Constrained Minimization Problem in Compressive Sensing. Circuits, Systems, and Signal Processing, 36, 4022-4049.
https://doi.org/10.1007/s00034-017-0498-5
[8]  Xiao, Y.H. and Zhu, H. (2013) A Conjugate Gradient Method to Solve Convex Constrained Monotone Equations with Applications in Compressive Sensing. Journal of Mathematical Anal- ysis and Applications, 405, 310-319.
https://doi.org/10.1016/j.jmaa.2013.04.017
[9]  Sun HC, Sun M and Zhang BH. (2018) An Inverse Matrix-Free Proximal Point Algorithm for Compressive Sensing. Science Asia, 44, 311-318.
https://doi.org/10.2306/scienceasia1513-1874.2018.44.311
[10]  Masao Fukushima. 非线性最优化基础[M]. 林贵华, 译. 北京: 科学出版社, 2011: 5.
[11]  Ortega J. M. and Rheinboldt W. C. (2000) Iterative Solution of Nonlinear Equations in Several Variables. In: Classics in Applied Mathematics, Vol. 30, SIAM, Philadelphia.

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