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Multimedia and Ubiquitous Engineering pp 997–1003Cite as

A Filter Selection Method in Hard Thresholding Recovery for Compressed Image Sensing

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 240))

Abstract

Compressed sensing has been widely researched since the beginning of 2000s. Although there are several well-known signal recovery algorithms, its reconstruction noise cannot be avoided completely, thus requiring good filters to remove the noise in the reconstructing process. Since each different filter has its own advantages and disadvantages depending on specific reconstruction algorithm, the reconstruction performance can be varied according to the choice of filter. This paper proposes an inner filter selection method according to the sampling rate and the property of image to be sensed.

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References

  1. Cover TM, Thomas JA (2006) Elements of information theory, 2nd edn. Wiley, New York

    MATH  Google Scholar 

  2. Donoho DL (2006) Compressed sensing. IEEE Trans Inform Theory 52(4): 1289–1306

    Google Scholar 

  3. Candes EJ, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inform Theory 52(2): 489–509

    Google Scholar 

  4. Baraniuk RG (2007) Compressive sensing [lecture notes]. IEEE Signal Process Mag 24(4):118–121

    Article  Google Scholar 

  5. Fowler JE, Mun S, Tramel EW (2012) Block–Based compressed sensing of image and video. Found Trends Signal Process 4(4):297–416

    Article  Google Scholar 

  6. Kumar S, Kumar P, Gupta M, Nagawat AK (2010) Performance comparison of median and Wiener filter in image de-noising. Int J Comput Appl (0975–8887) 12(4): 27–31

    Google Scholar 

  7. Candes E, Romberg J (2005) ℓ1-magic: recovery of sparse signals via convex programming. Technical report, California Institute of Technology

    Google Scholar 

  8. Figueiredo MAT, Nowak RD, Wright SJ (2007) Gradient Projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE J Sel Topics Signal Process 1(4):586–597

    Article  Google Scholar 

  9. Chen SS, Donoho DL, Saunders MA (1998) Atomic decomposition by Basis Pursuit. SIAM J Sci Comput 20(1):33–61

    Article  MathSciNet  Google Scholar 

  10. Ji S, Xue Y, Carin L (2008) Bayesian compressive sensing. IEEE Trans Signal Process 56(6):2346–2356

    Article  MathSciNet  Google Scholar 

  11. Khireddine AK, Benmahammed, Puech W (2007) Digital image restoration by Wiener filter in 2D case. Adv Eng Softw 38(7):513–516

    Article  Google Scholar 

  12. Church JC, Yixin C, Rice SV (2008) A spatial median filter for noise removal in digital images. In: IEEE Southeastcon, Alabama, pp 618–623

    Google Scholar 

Download references

Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2011-001-7578).

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, Sungkyunkwan University, Seoul, Korea

    Phuong Minh Pham, Khanh Quoc Dinh & Byeungwoo Jeon

Authors
  1. Phuong Minh Pham
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  2. Khanh Quoc Dinh
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  3. Byeungwoo Jeon
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Corresponding author

Correspondence to Phuong Minh Pham .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Seoul University of Science & and Technology (SeoulTech), Seoul, Korea, Republic of (South Korea)

    James J. (Jong Hyuk) Park

  2. Dept of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR

    Joseph Kee-Yin Ng

  3. Humanitas College, Kyung Hee University, Seoul, Korea, Republic of (South Korea)

    Hwa-Young Jeong

  4. School of Computer Science and Software Engineering, Monash University, Clayton, Victoria, Australia

    Borgy Waluyo

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Pham, P.M., Dinh, K.Q., Jeon, B. (2013). A Filter Selection Method in Hard Thresholding Recovery for Compressed Image Sensing. In: Park, J., Ng, JY., Jeong, HY., Waluyo, B. (eds) Multimedia and Ubiquitous Engineering. Lecture Notes in Electrical Engineering, vol 240. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6738-6_123

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  • DOI: https://doi.org/10.1007/978-94-007-6738-6_123

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-6737-9

  • Online ISBN: 978-94-007-6738-6

  • eBook Packages: EngineeringEngineering (R0)

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