Advertisement

Nonlinear Dynamics

, Volume 94, Issue 1, pp 295–305 | Cite as

When underwater degraded images meet logical stochastic resonance

  • Nan Wang
  • Bing Zheng
  • Haiyong Zheng
  • Biao Yang
Original Paper
  • 112 Downloads

Abstract

Owing to light attenuation and high background noise, underwater images are significantly degraded, which hiders the development of underwater exploration. However, noise itself can be used to counter noise. In this paper, we apply logical stochastic resonance (LSR) to help detect weak objects from low-quality underwater images. On the basis of analysis of the physical character of underwater images, three models, namely basic dynamical system driven by Gaussian noise, basic dynamical system driven by Ornstein–Uhlenbeck (OU) noise, and dynamical system with extra delay loop, are chosen to study the performance of LSR-based object detection. The main workflow of LSR-based object detection is introduced. To analyze the performance of LSR, we perform explicit experiments and systematically discuss the interplay of additional noise with the system parameters. LSR is proven to be helpful in detecting weak objects from low-quality underwater images. Both OU noise and extra delay loop will help the whole system to maintain stability in a higher noisy background.

Keywords

Logical stochastic resonance Weak object detection Underwater image processing 

Notes

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest concerning the publication of this manuscript

References

  1. 1.
    Duntley, S.Q.: Light in the sea. JOSA 53(2), 214 (1963)CrossRefGoogle Scholar
  2. 2.
    Raimondo, S., Silvia, C.: Underwater image processing: state of the art of restoration and image enhancement methods. EURASIP J. Adv. Signal Process. 2010(1), 1–15 (2010)Google Scholar
  3. 3.
    Jaffe, J.S.: Underwater optical imaging: the past, the present, and the prospects. IEEE J. Ocean. Eng. 40(3), 683 (2015)CrossRefGoogle Scholar
  4. 4.
    Chiang, J.Y., Chen, Y.C.: Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans. Image Process. 21(4), 1756 (2012)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Benzi, R., Sutera, A., Vulpiani, A.: The mechanism of stochastic resonance. J. Phys. A Math. Gen. 14(11), 453 (1981)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Chen, H., Varshney, L.R., Varshney, P.K.: Noise-enhanced information systems. Proc. IEEE 102(10), 1607 (2014)CrossRefGoogle Scholar
  7. 7.
    Harmer, G., Davis, B., Abbott, D.: A review of stochastic resonance: circuits and measurement. IEEE Trans. Instrum. Meas. 51(2), 299 (2002)CrossRefGoogle Scholar
  8. 8.
    Hnggi, P.: Stochastic resonance in biology how noise can enhance detection of weak signals and help improve biological information processing. Chemphyschem A Eur. J. Chem. Phys. Phys. Chem. 3(3), 285 (2002)CrossRefGoogle Scholar
  9. 9.
    Mitaim, S., Kosko, B.: Adaptive stochastic resonance in noisy neurons based on mutual information. IEEE Trans. Neural Netw. 15(6), 1526 (2004)CrossRefGoogle Scholar
  10. 10.
    Dylov, D.V., Fleischer, J.W.: Nonlinear self-filtering of noisy images via dynamical stochastic resonance. Nat. Photonics 4(5), 323 (2010)CrossRefGoogle Scholar
  11. 11.
    Monifi, F., Zhang, J., Özdemir, Ş.K., Peng, B., Liu, Y., Bo, F., Nori, F., Yang, L.: Optomechanically induced stochastic resonance and chaos transfer between optical fields. Nat. Photonics 10(6), 399 (2016)CrossRefGoogle Scholar
  12. 12.
    Jha, R.K., Chouhan, R.: Noise-induced contrast enhancement using stochastic resonance on singular values. Signal Image Video Process. 8(2), 339 (2014)CrossRefGoogle Scholar
  13. 13.
    Ryu, C., Kong, S.G., Kim, H.: Enhancement of feature extraction for low-quality fingerprint images using stochastic resonance. Pattern Recognit. Lett. 32(2), 107 (2011)CrossRefGoogle Scholar
  14. 14.
    Jha, R.K., Biswas, P.K., Shrivastava, S.: Logo extraction using dynamic stochastic resonance. Signal Image Video Process. 7(1), 119 (2013)CrossRefGoogle Scholar
  15. 15.
    Rallabandi, V.P., Roy, P.K.: Magnetic resonance image enhancement using stochastic resonance in Fourier domain. Magn. Reson. Imag. 28(9), 1361 (2010)CrossRefGoogle Scholar
  16. 16.
    Yang, J.H., Sanjun, M.A.F., Liu, H.G., Zhu, H.: Noise-induced resonance at the subharmonic frequency in bistable systems. Nonlinear Dyn. 87(3), 1721 (2017)CrossRefGoogle Scholar
  17. 17.
    Hu, B., Kurths, J., Zhou, C.: Array-enhanced coherence resonance. Nature 437(7059), 601 (2001)Google Scholar
  18. 18.
    Rajamani, S., Rajasekar, S., Sanjuán, M.A.F.: Ghost-vibrational resonance. Commun. Nonlinear Sci. Numer. Simul. 19(11), 4003 (2014)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Murali, K., Rajamohamed, I., Sinha, S., Ditto, W.L., Bulsara, A.R.: Realization of reliable and flexible logic gates using noisy nonlinear circuits. Appl. Phys. Lett. 95(19), 194102 (2009)CrossRefGoogle Scholar
  20. 20.
    Murali, K., Sinha, S., Ditto, W.L., Bulsara, A.R.: Reliable logic circuit elements that exploit nonlinearity in the presence of a noise floor. Phys. Rev. Lett. 102(10), 194102 (2009)CrossRefGoogle Scholar
  21. 21.
    Gupta, A., Sohane, A., Kohar, V., Murali, K., Sinha, S.: Noise-free logical stochastic resonance. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 84(2), 055201 (2011)CrossRefGoogle Scholar
  22. 22.
    Yang, B., Zhang, X., Luo, M.: When noise-free logical stochastic resonance occurs in a bistable system. Nonlinear Dyn. 87(3), 1957 (2017)CrossRefGoogle Scholar
  23. 23.
    Sharma, A., Kohar, V., Shrimali, M.D., Sinha, S.: Realizing logic gates with time-delayed synthetic genetic networks. Nonlinear Dyn. 76(1), 431 (2013)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Wang, N., Song, A.: Enhanced logical stochastic resonance in synthetic genetic networks. IEEE Trans. Neural Netw. Learn. Syst. 27(12), 2736 (2016)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Zhang, H., Yang, T., Xu, W., Xu, Y.: Effects of non-Gaussian noise on logical stochastic resonance in a triple-well potential system. Nonlinear Dyn. 76(1), 649 (2014)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Kohar, V., Murali, K., Sinha, S.: Enhanced logical stochastic resonance under periodic forcing. Commun. Nonlinear Sci. Numer. Simul. 19(8), 2866 (2014)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Wang, G., Zheng, B., Sun, F.F.: Estimation-based approach for underwater image restoration. Opt. Lett. 36(13), 2384 (2011)CrossRefGoogle Scholar
  28. 28.
    Gammaitoni, L., Hänggi, P., Jung, P., Marchesoni, F.: Stochastic resonance. Rev. Mod. Phys. 70(1), 223 (1998)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Ocean University of ChinaQingdaoChina
  2. 2.ChangZhou UniversityChangzhouChina

Personalised recommendations