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An Adaptive Iteratively Weighted Half Thresholding Algorithm for Image Compressive Sensing Reconstruction

  • Qiwei Peng
  • Tongwei Yu
  • Wang LuoEmail author
  • Tong Li
  • Gaofeng Zhao
  • Qiang Fan
  • Xiaolong Hao
  • Peng Wang
  • Zhiguo Li
  • Qilei Zhong
  • Min Feng
  • Lei Yu
  • Tingliang Yan
  • Shaowei Liu
  • Yuan Xia
  • Bin Han
  • Qibin Dai
  • Yunyi Li
  • Zhenyue Zhang
  • Guan Gui
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

The \( L_{1/2} \) regularization has been considered as a more effective relaxation method to approximate the optimal \( L_{0} \) sparse solution than \( L_{1} \) in CS. To improve the recovery performance of \( L_{1/2} \) regularization, this study proposes a multiple sub-wavelet-dictionaries-based adaptive iteratively weighted \( L_{1/2} \) regularization algorithm (called MUSAI-\( L_{1/2} \)), and considering the key rule of the weighted parameter (or regularization parameter) in optimization progress, we propose the adaptive scheme for parameter \( \lambda_{d} \) to weight the regularization term which is a composition of the sub-dictionaries. Numerical experiments confirm that the proposed MUSAI-\( L_{1/2} \) can significantly improve the recovery performance than the previous works.

Keywords

L1/2 regularization Multiple sub-wavelet-dictionaries Enhancing sparsity Adaptive Iteratively weighted 

Notes

Acknowledgements

This research was funded by State Grid Corporation Science and Technology Project (named ‘Research on intelligent patrol and inspection technology of substation robot based on intelligent sensor active collaboration technology’).

References

  1. 1.
    Guo J, Song B, He Y, Yu FR, Sookhak M. A survey on compressed sensing in vehicular infotainment systems. IEEE Commun Surv Tutorials. 2017;19(4):2662–80.CrossRefGoogle Scholar
  2. 2.
    Luo Y, Wan Q, Gui G, Adachi F. A matching pursuit generalized approximate message passing algorithm. IEICE Trans Fundam Electron Commun Comput Sci. 2015;E98–A(12):2723–7.CrossRefGoogle Scholar
  3. 3.
    Gao Z, Dai L, Han S, Chih-Lin I, Wang Z, Hanzo L. Compressive sensing techniques for next-generation wireless communications. IEEE Wirel Commun. 2018; 2–11.Google Scholar
  4. 4.
    Shi Z, Zhou C, Gu Y, Goodman NA, Qu F. Source estimation using coprime array: a sparse reconstruction perspective. IEEE Sens J. 2017;17(3):755–65.CrossRefGoogle Scholar
  5. 5.
    Hayashi K, Nagahara M, Tanaka T. A user’s guide to compressed sensing for communications systems. IEICE Trans Commun. 2013;E96–B(3):685–712.CrossRefGoogle Scholar
  6. 6.
    Donoho DL. High-dimensional centrally symmetric polytopes with neighborliness proportional to dimension. Discret Comput Geom. 2006;35(January):617–52.MathSciNetCrossRefGoogle Scholar
  7. 7.
    Yilmaz O. Stable sparse approximations via nonconvex optimization. In IEEE international conference on acoustics, speech and signal processing, 2007; 2008. p. 3885–8.Google Scholar
  8. 8.
    Chartrand R. Nonconvex compressed sensing and error correction. In: IEEE international conference on acoustics, speech and signal processing 2007; 2007, no. 3. p. 889–92.Google Scholar
  9. 9.
    Chartrand R. Exact reconstruction of sparse signals via nonconvex minimization. IEEE Sig Process Lett. 2007;14(10):707–10.CrossRefGoogle Scholar
  10. 10.
    Xu Z, Chang X, Xu F, Zhang H. L1/2 regularization: a thresholding representation theory and a fast solver. IEEE Trans Neural Netw Learn Syst. 2012;23(7):1013–27.CrossRefGoogle Scholar
  11. 11.
    Ahmad R, Schniter P. Iteratively reweighted L1 approaches to sparse composite regularization. IEEE Trans Comput Imaging. 2015;1(4):220–35.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Qiwei Peng
    • 1
  • Tongwei Yu
    • 2
  • Wang Luo
    • 1
    Email author
  • Tong Li
    • 2
  • Gaofeng Zhao
    • 1
  • Qiang Fan
    • 1
  • Xiaolong Hao
    • 1
  • Peng Wang
    • 1
  • Zhiguo Li
    • 1
  • Qilei Zhong
    • 1
  • Min Feng
    • 1
  • Lei Yu
    • 1
  • Tingliang Yan
    • 1
  • Shaowei Liu
    • 1
  • Yuan Xia
    • 1
  • Bin Han
    • 1
  • Qibin Dai
    • 1
  • Yunyi Li
    • 3
  • Zhenyue Zhang
    • 3
  • Guan Gui
    • 3
  1. 1.NARI Group Corporation/State Grid Electric Power Research InstituteNanjingChina
  2. 2.State Grid Liaoning Electric Power Supply Co. Ltd.ShenyangChina
  3. 3.Nanjing University of Posts and TelecommunicationsNanjingChina

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