求解压缩传感问题的一种投影算法
A Projection Algorithm for Compressive Sensing
DOI: 10.12677/ORF.2015.51001, PDF, HTML, XML, 下载: 2,478  浏览: 6,340 
作者: 于丽超, 屈 彪：曲阜师范大学管理学院，山东 日照
关键词: 凸可行问题；压缩传感；投影算法；Convex Feasibility Problem； Compressed Sensing； Projection Method
摘要: 本文在将压缩传感的最优化问题转化为凸可行问题的基础上，设计了一种投影算法来求解凸可行问题，进而来求解压缩传感问题。
Abstract: In the paper, we first transform the optimization problem of compressed sensing into a convex feasibility problem. Then, a projection method is presented to solve it.
文章引用：于丽超, 屈彪. 求解压缩传感问题的一种投影算法[J]. 运筹与模糊学, 2015, 5(1): 1-5. http://dx.doi.org/10.12677/ORF.2015.51001

参考文献

[1] Donoho, D.L. (2006) Compressed sensing. IEEE Transactions on Information Theory, 52, 1289-1306.
[2] Candes, E., Romberg, J. and Tao, T. (2006) Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52, 489-509.
[3] 杨凡 (2010) 压缩传感图像重建的研究. 硕士论文, 江西科技师范学院, 南昌.
[4] Gamper, U., Boesiger, P. and Kozerke, S. (2008) Compressed sensing in dynamic MRI. Magnetic Resonance in Medicine, 59, 365-373.
[5] Carmi, A., Censor, Y. and Gurfil, P. (2012) Convex feasibility modeling and projection methods for sparse signal recovery. Journal of Computational and Applied Mathematics, 236, 4318-4335.
[6] Boyd, S. and Vandenberghe, L. (2009) Convex optimization. Cambridge University Press, New York.
[7] Zarantonello, E.H. (1971) Projections on convex sets in Hilbert space and spectral theory. In: Zarantonello, E.H., Ed., Contributions to Nonlinear Functional Analysis, Academic Press, New York, 237-424.

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