Advertisement

The Design and Implementation of Linear Array Image Acquisition System

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 246)

Abstract

The theory of Compressive Sensing is briefly introduced and the design of the linear array image acquisition system which is based on the Compressive Sensing is deduced. The system design includes two aspects: the hardware design and the software design. The core components of the system and the structure of the circuit are introduced. The image acquisition program which can construct the measurement matrix of Compressive Sensing and process the image signal is devised. Numerical simulations have verified that the linear array image acquisition system has good performance in linear array image acquisition and reconstruction.

Keywords

Compressive sensing Linear array image acquisition system Core components Measurement matrix 

Notes

Acknowledgment

This research was supported by the Tianjin Younger Natural Science Foundation(12JCQNJC00400). National Science Foundation of China: 61271411.

References

  1. 1.
    Rivenson Y, Stern A (2009) Compressed imaging with a separable sensing operator. IEEE Signal Process Lett 16(6):449–452CrossRefGoogle Scholar
  2. 2.
    He L, Carin L (2009) Exploiting structure in wavelet-based Bayesian compressive sensing. IEEE Trans Signal Process 57(9):3488–3497CrossRefMathSciNetGoogle Scholar
  3. 3.
    Lustig M, Donoho DL, Pauly JM (2007) Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 58(6):1182–1195CrossRefGoogle Scholar
  4. 4.
    Yoon-Chul Kim, Narayanan SS et al (2009) Accelerated three-dimensional upper airway. MRI using compressed sensing. Magn Reson Med 61:1434–1440Google Scholar
  5. 5.
    Xie Xiao-Chun, Zhang Yun-Hua (2010) High-resolution imaging of moving train by ground-based radar with compressive sensing. Electron Lett 46(7):529–531Google Scholar
  6. 6.
    Budillon A, Evangelista A, Schirinzi G (2011) Three-dimensional SAR focusing from multipass signals using compressive sampling. IEEE Trans Geosci Rem Sens 49:488–499CrossRefGoogle Scholar
  7. 7.
    Wai Lam Chan, Kriti Charan, Dharmpal Takhar et al (2008) A single-pixel terahertz imaging system based on compressed sensing. Appl Phys Lett 93, 121105:1–3Google Scholar
  8. 8.
    Amir Averbuch, Shai Dekel, Shay Deutsch (2012) Adaptive compressed image sensing using dictionaries. SIAM J Imag Sci 5:57–89Google Scholar
  9. 9.
    Marcia RF, Willett RM (2008) Compressive coded aperture video reconstruction. Proceedings of 16th European signal processing conference, Lausanne, Switzerland, pp 918–923Google Scholar
  10. 10.
    Stern A, Yair Rivenson, Bahram Javidi (2008) Single exposure optically compressed imaging and visualization using random aperture coding. J Phys Conf Ser 139:1–10Google Scholar
  11. 11.
    Fergus R, Torralba A, Freeman WT (2006) Random lens imaging. MIT Comput Sci Artif Intell Lab Rep 9:111–118Google Scholar
  12. 12.
    Ji Wu, Wei Wang, Qilian Liang, Xiaorong Wu, Baoju Zhang (2012) Compressive sensing-based signal compression and recovery in UWB wireless communication system, Wiley wireless communications and mobile computing, DOI:  10.1002/wcm.2228

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.College of Electronic and CommunicationTianjin Normal UniversityTianjinChina

Personalised recommendations