Multimedia Tools and Applications

, Volume 77, Issue 6, pp 7595–7613 | Cite as

Noise performance of non-iterative compressed sensing based recovery algorithm: surveillance applications

  • J. Florence Gnana Poovathy
  • S. Radha


Compressed sensing has been of great interest in signal compression since it promises higher compression level and ease in usage. It is widely used in signal processing domain for compression and reconstruction of various signals including electrical signals, images, videos, etc. The concept of compressed sensing can be applied suitably for surveillance videos since voluminous video quantities can be significantly compressed and retrieved perfectly. The surveillance videos are prone to various noises like impulse noise, quantization noise, multiplicative noise, etc., that raise as hindrance to high quality video reconstruction. Thus, non-iterative compressed sensing based recovery algorithm is proposed that recovers the surveillance videos with higher perfection in the presence of various noises. The algorithm uses augmented matrix as sensing matrix and hence avoids iterations leading to commendable reduction in runtime. The signal to noise ratio obtained using the proposed algorithm is ~39 dB which is greater than any other existing noise removing CS recovery algorithms like OMP-CV, TMSBL, etc. High speed recovery is made possible due to the absence of iterations. Accuracy and structural similarity obtained are nearly 98% and 95% respectively. The algorithm is robust to various noise levels and the hardware implementation shows that the algorithm is simple enough to be used in hardware of lower specifications. These results ensure NIPIRA as a best suitor for real time surveillance video reconstruction even in the presence of noise.


Compressed sensing Non-iterative reconstruction Noise Measurements Surveillance 


  1. 1.
    Boufounos P, Duarte MF, Baraniuk RG (2007) Sparse signal reconstruction from noisy compressive measurements using cross validation. In 2007 IEEE/SP 14th workshop on statistical signal processing (pp. 299–303). IEEEGoogle Scholar
  2. 2.
    Cai TT, Wang L (2011) Orthogonal matching pursuit for sparse signal recovery with noise. IEEE Trans Inf Theory 57(7):4680–4688MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Candès EJ, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489–509MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Donoho DL, Tsaig Y, Drori I, Starck JL (2012) Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit. IEEE Trans Inf Theory 58(2):1094–1121MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Drori I (2008) Compressed video sensing. In BMVA symposium on 3D video-analysis, display, and applicationsGoogle Scholar
  6. 6.
    Florence Gnana Poovathy J and Radha S (2015) Reduced runtime recovery algorithm for compressively sensed images. In Proc. of 2nd International Conference on Next Generation Computing and Communication Technologies (ICNGCCT 2015), 22–23, Deira, 128–132Google Scholar
  7. 7.
    Florence Gnana Poovathy J, Radha S (2017) Efficient reconstruction of compressively sensed images and videos using non-iterative method. AEU Int J Electron Commun 73:89–97CrossRefGoogle Scholar
  8. 8.
    Khambete M, Joshi M (2007) Blur and ringing artifact measurement in image compression using wavelet transform. In Proceedings of World Academy of Science, Engineering and Technology 20, 183–186, p. 1Google Scholar
  9. 9.
    Kratochvil T, Simicek P (2005) Utilization of Matlab for picture quality evaluation. Proc. Institute of Radio ElectronicsGoogle Scholar
  10. 10.
    MacKenzie D (2009) Compressed sensing makes every pixel count. What’s Happening in the Mathematical Sciences 7:114–127Google Scholar
  11. 11.
    Meiniel W, Le Montagner Y, Angelini E, Olivo-Marin JC (2015) Image denoising by multiple compressed sensing reconstructions. In 2015 I.E. 12th international symposium on biomedical imaging (ISBI) (pp. 1232–1235). IEEEGoogle Scholar
  12. 12.
    Metzler CA, Maleki A, Baraniuk RG (2016) From denoising to compressed sensing. IEEE Trans Inf Theory 62(9):5117–5144Google Scholar
  13. 13.
    Mrak M, Grgic S, Grgic M (2003) Picture quality measures in image compression systems. In EUROCON 2003. Computer as a tool. The IEEE region 8 (Vol. 1, pp. 233–236). IEEEGoogle Scholar
  14. 14.
    Poovathy J, Radha S (2015) Non-iterative threshold based recovery algorithm (NITRA) for compressively sensed images and videos. KSII Trans Internet Inf Syst 9(10):4160–4176Google Scholar
  15. 15.
    Sturm BL, Græsbøll Christensen M (2010) Comparison of orthogonal matching pursuit implementations. 20th European Signal Processing Conference (EUSIPCO 2012). Romania, August 27–31Google Scholar
  16. 16.
    Suganesh V, Florence Gnana Poovathy J, Radha S (2016) Filtering of gaussian filter based embedded enhancement technique for compressively sensed images. International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), IEEE, India, March 23–25Google Scholar
  17. 17.
    Tavakoli A, Pourmohammad A (2012) Image denoising based on compressed sensing. International Journal of Computer Theory and Engineering 4(2):266CrossRefGoogle Scholar
  18. 18.
    Verma R, Ali DJ (2013) A comparative study of various types of image noise and efficient noise removal techniques. International Journal of Advanced Research in Computer Science and Software Engineering 3(10):617–622Google Scholar
  19. 19.
    WINGZ Energy Board, Centre for development of advanced computing No.1, Old Madras Road, Byappanahalli, Bangalore-560038Google Scholar
  20. 20.
    Zhang Z (2012). Comparison of sparse signal recovery algorithms with highly coherent dictionary matrices: the advantage of T-MSBL. Research noteGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Electronics and Communications EngineeringSri Sivasubramaniya Nadar College of EngineeringChennaiIndia

Personalised recommendations