Abstract

Feeble object detection is a long-standing problem in vision based underwater exploration work. However, because of the complicated light propagation situation and high background noise, underwater images are highly degraded. Noise is not always detrimental. Logical stochastic resonance (LSR) can be a useful tool for amplifying feeble signals by utilizing the constructive interplay of noise and a nonlinear system. In the present study, an appropriate LSR structure with a delay loop is proposed to process a low-quality underwater image for enhancing the vision detection accuracy of underwater feeble objects. Ocean experiments are conducted to demonstrate the effectiveness of the proposed structure. We also give explicit numerical results to illustrate the relationship between the structure of LSR and the correct detection probability. Methods presented in this paper are quite general and can thus be potentially extended to other applications for obtaining better performance.

© 2017 Optical Society of America

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  1. S. Raimondo and C. Silvia, “Underwater image processing: State of the art of restoration and image enhancement methods,” EURASIP J. Adv. Signal Processing pp. 1–15 (2010).
  2. J. S. Jaffe, “Underwater optical imaging: the past, the present, and the prospects,” IEEE J. Oceanic Eng. 40, 683–700 (2015).
    [Crossref]
  3. G. Wang, B. Zheng, and F. F. Sun, “Estimation-based approach for underwater image restoration,” Opt. Lett. 36, 2384–2386 (2011).
    [Crossref] [PubMed]
  4. B. Huang, T. Liu, H. Hu, J. Han, and M. Yu, “Underwater image recovery considering polarization effects of objects,” Opt. Express 24, 9826–9838 (2016).
    [Crossref] [PubMed]
  5. J. Y. Chiang and Y.-C. Chen, “Underwater image enhancement by wavelength compensation and dehazing,” IEEE Trans. Image Process. 21, 1756–1769 (2012).
    [Crossref]
  6. Y. Li, H. Lu, J. Li, X. Li, Y. Li, and S. Serikawa, “Underwater image de-scattering and classification by deep neural network,” Comput. Electrical Eng. 54, 68–77 (2016).
    [Crossref]
  7. R. Benzi, A. Sutera, and A. Vulpiani, “The mechanisim of stochastic resonance,” J. Phys. A: Mathmatical Genenral 14, 453–457 (1981).
    [Crossref]
  8. K. Murali, I. Rajamohamed, S. Sinha, W. L. Ditto, and A. R. Bulsara, “Realization of reliable and flexible logic gates using noisy nonlinear circuits,” Appl. Phys. Lett. 95, 194102 (2009).
    [Crossref]
  9. K. Murali, S. Sinha, W. L. Ditto, and A. R. Bulsara, “Reliable logic circuit elements that exploit nonlinearity in the presence of a noise floor,” Phys. Rev. Lett. 102, 194102 (2009).
    [Crossref]
  10. N. Wang and A. Song, “Set-reset latch logical operation induced by colored noise,” Phys. Lett. A 378, 1588–1592 (2014).
    [Crossref]
  11. N. Wang and A. Song, “Parameter-induced logical stochastic resonance,” Neurocomputing 155, 80–83 (2015).
    [Crossref]
  12. H. Zhang, T. Yang, W. Xu, and Y. Xu, “Effects of non-gaussian noise on logical stochastic resonance in a triple-well potential system,” Nonlinear Dynamics 76, 649–656 (2014).
    [Crossref]
  13. L. Worschech, F. Hartmann, T. Kim, S. Höfling, M. Kamp, A. Forchel, J. Ahopelto, I. Neri, A. Dari, and L. Gammaitoni, “Universal and reconfigurable logic gates in a compact three-terminal resonant tunneling diode,” Appl. Phys. Lett. 96, 042112 (2010).
    [Crossref]
  14. D. N. Guerra, A. R. Bulsara, W. L. Ditto, S. Sinha, K. Murali, and P. Mohanty, “A noise-assisted reprogrammable nanomechanical logic gate,” Nano Lett. 10, 1168–1171 (2010).
    [Crossref] [PubMed]
  15. N. Wang and A. Song, “Enhanced logical stochastic resonance in synthetic genetic networks,” IEEE Trans. Neural Networks Learning Systems 12, 2735–2739 (2015).
  16. A. Sharma, V. Kohar, M. D. Shrimali, and S. Sinha, “Realizing logic gates with time-delayed synthetic genetic networks,” Nonlinear Dynamics 76, 431–439 (2014).
    [Crossref]
  17. H. Chen, L. R. Varshney, and P. K. Varshney, “Noise-enhanced information systems,” Proc. IEEE 102, 1607–1621 (2014).
    [Crossref]
  18. B. Zheng, N. Wang, H. Zheng, Z. Yu, and J. Wang, “Object extraction from underwater images through logical stochastic resonance,” Opt. Lett. 41, 4967–4970 (2016).
    [Crossref] [PubMed]
  19. S. Q. Duntley, “Light in the sea,” J. Opt. Soc. Am. 53, 214–233 (1963).
    [Crossref]
  20. L. Gammaitoni, P. Hänggi, P. Jung, and F. Marchesoni, “Stochastic resonance,” Rev. Mod. Phys. 70, 45–105 (1998).
    [Crossref]
  21. T. Frank, “Delay fokker-planck equations, novikovs theorem, and boltzmann distributions as small delay approximations,” Phys. Rev. E 72, 011112 (2005).
    [Crossref]
  22. K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Analysis Machine Intelligence 33, 2341 (2011).
  23. R. Fattal, “Single image dehazing,” ACM Trans. Graphics 27, 72 (2008).
    [Crossref]

2016 (3)

2015 (3)

N. Wang and A. Song, “Enhanced logical stochastic resonance in synthetic genetic networks,” IEEE Trans. Neural Networks Learning Systems 12, 2735–2739 (2015).

N. Wang and A. Song, “Parameter-induced logical stochastic resonance,” Neurocomputing 155, 80–83 (2015).
[Crossref]

J. S. Jaffe, “Underwater optical imaging: the past, the present, and the prospects,” IEEE J. Oceanic Eng. 40, 683–700 (2015).
[Crossref]

2014 (4)

H. Zhang, T. Yang, W. Xu, and Y. Xu, “Effects of non-gaussian noise on logical stochastic resonance in a triple-well potential system,” Nonlinear Dynamics 76, 649–656 (2014).
[Crossref]

A. Sharma, V. Kohar, M. D. Shrimali, and S. Sinha, “Realizing logic gates with time-delayed synthetic genetic networks,” Nonlinear Dynamics 76, 431–439 (2014).
[Crossref]

H. Chen, L. R. Varshney, and P. K. Varshney, “Noise-enhanced information systems,” Proc. IEEE 102, 1607–1621 (2014).
[Crossref]

N. Wang and A. Song, “Set-reset latch logical operation induced by colored noise,” Phys. Lett. A 378, 1588–1592 (2014).
[Crossref]

2012 (1)

J. Y. Chiang and Y.-C. Chen, “Underwater image enhancement by wavelength compensation and dehazing,” IEEE Trans. Image Process. 21, 1756–1769 (2012).
[Crossref]

2011 (2)

G. Wang, B. Zheng, and F. F. Sun, “Estimation-based approach for underwater image restoration,” Opt. Lett. 36, 2384–2386 (2011).
[Crossref] [PubMed]

K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Analysis Machine Intelligence 33, 2341 (2011).

2010 (2)

L. Worschech, F. Hartmann, T. Kim, S. Höfling, M. Kamp, A. Forchel, J. Ahopelto, I. Neri, A. Dari, and L. Gammaitoni, “Universal and reconfigurable logic gates in a compact three-terminal resonant tunneling diode,” Appl. Phys. Lett. 96, 042112 (2010).
[Crossref]

D. N. Guerra, A. R. Bulsara, W. L. Ditto, S. Sinha, K. Murali, and P. Mohanty, “A noise-assisted reprogrammable nanomechanical logic gate,” Nano Lett. 10, 1168–1171 (2010).
[Crossref] [PubMed]

2009 (2)

K. Murali, I. Rajamohamed, S. Sinha, W. L. Ditto, and A. R. Bulsara, “Realization of reliable and flexible logic gates using noisy nonlinear circuits,” Appl. Phys. Lett. 95, 194102 (2009).
[Crossref]

K. Murali, S. Sinha, W. L. Ditto, and A. R. Bulsara, “Reliable logic circuit elements that exploit nonlinearity in the presence of a noise floor,” Phys. Rev. Lett. 102, 194102 (2009).
[Crossref]

2008 (1)

R. Fattal, “Single image dehazing,” ACM Trans. Graphics 27, 72 (2008).
[Crossref]

2005 (1)

T. Frank, “Delay fokker-planck equations, novikovs theorem, and boltzmann distributions as small delay approximations,” Phys. Rev. E 72, 011112 (2005).
[Crossref]

1998 (1)

L. Gammaitoni, P. Hänggi, P. Jung, and F. Marchesoni, “Stochastic resonance,” Rev. Mod. Phys. 70, 45–105 (1998).
[Crossref]

1981 (1)

R. Benzi, A. Sutera, and A. Vulpiani, “The mechanisim of stochastic resonance,” J. Phys. A: Mathmatical Genenral 14, 453–457 (1981).
[Crossref]

1963 (1)

Ahopelto, J.

L. Worschech, F. Hartmann, T. Kim, S. Höfling, M. Kamp, A. Forchel, J. Ahopelto, I. Neri, A. Dari, and L. Gammaitoni, “Universal and reconfigurable logic gates in a compact three-terminal resonant tunneling diode,” Appl. Phys. Lett. 96, 042112 (2010).
[Crossref]

Benzi, R.

R. Benzi, A. Sutera, and A. Vulpiani, “The mechanisim of stochastic resonance,” J. Phys. A: Mathmatical Genenral 14, 453–457 (1981).
[Crossref]

Bulsara, A. R.

D. N. Guerra, A. R. Bulsara, W. L. Ditto, S. Sinha, K. Murali, and P. Mohanty, “A noise-assisted reprogrammable nanomechanical logic gate,” Nano Lett. 10, 1168–1171 (2010).
[Crossref] [PubMed]

K. Murali, I. Rajamohamed, S. Sinha, W. L. Ditto, and A. R. Bulsara, “Realization of reliable and flexible logic gates using noisy nonlinear circuits,” Appl. Phys. Lett. 95, 194102 (2009).
[Crossref]

K. Murali, S. Sinha, W. L. Ditto, and A. R. Bulsara, “Reliable logic circuit elements that exploit nonlinearity in the presence of a noise floor,” Phys. Rev. Lett. 102, 194102 (2009).
[Crossref]

Chen, H.

H. Chen, L. R. Varshney, and P. K. Varshney, “Noise-enhanced information systems,” Proc. IEEE 102, 1607–1621 (2014).
[Crossref]

Chen, Y.-C.

J. Y. Chiang and Y.-C. Chen, “Underwater image enhancement by wavelength compensation and dehazing,” IEEE Trans. Image Process. 21, 1756–1769 (2012).
[Crossref]

Chiang, J. Y.

J. Y. Chiang and Y.-C. Chen, “Underwater image enhancement by wavelength compensation and dehazing,” IEEE Trans. Image Process. 21, 1756–1769 (2012).
[Crossref]

Dari, A.

L. Worschech, F. Hartmann, T. Kim, S. Höfling, M. Kamp, A. Forchel, J. Ahopelto, I. Neri, A. Dari, and L. Gammaitoni, “Universal and reconfigurable logic gates in a compact three-terminal resonant tunneling diode,” Appl. Phys. Lett. 96, 042112 (2010).
[Crossref]

Ditto, W. L.

D. N. Guerra, A. R. Bulsara, W. L. Ditto, S. Sinha, K. Murali, and P. Mohanty, “A noise-assisted reprogrammable nanomechanical logic gate,” Nano Lett. 10, 1168–1171 (2010).
[Crossref] [PubMed]

K. Murali, I. Rajamohamed, S. Sinha, W. L. Ditto, and A. R. Bulsara, “Realization of reliable and flexible logic gates using noisy nonlinear circuits,” Appl. Phys. Lett. 95, 194102 (2009).
[Crossref]

K. Murali, S. Sinha, W. L. Ditto, and A. R. Bulsara, “Reliable logic circuit elements that exploit nonlinearity in the presence of a noise floor,” Phys. Rev. Lett. 102, 194102 (2009).
[Crossref]

Duntley, S. Q.

Fattal, R.

R. Fattal, “Single image dehazing,” ACM Trans. Graphics 27, 72 (2008).
[Crossref]

Forchel, A.

L. Worschech, F. Hartmann, T. Kim, S. Höfling, M. Kamp, A. Forchel, J. Ahopelto, I. Neri, A. Dari, and L. Gammaitoni, “Universal and reconfigurable logic gates in a compact three-terminal resonant tunneling diode,” Appl. Phys. Lett. 96, 042112 (2010).
[Crossref]

Frank, T.

T. Frank, “Delay fokker-planck equations, novikovs theorem, and boltzmann distributions as small delay approximations,” Phys. Rev. E 72, 011112 (2005).
[Crossref]

Gammaitoni, L.

L. Worschech, F. Hartmann, T. Kim, S. Höfling, M. Kamp, A. Forchel, J. Ahopelto, I. Neri, A. Dari, and L. Gammaitoni, “Universal and reconfigurable logic gates in a compact three-terminal resonant tunneling diode,” Appl. Phys. Lett. 96, 042112 (2010).
[Crossref]

L. Gammaitoni, P. Hänggi, P. Jung, and F. Marchesoni, “Stochastic resonance,” Rev. Mod. Phys. 70, 45–105 (1998).
[Crossref]

Guerra, D. N.

D. N. Guerra, A. R. Bulsara, W. L. Ditto, S. Sinha, K. Murali, and P. Mohanty, “A noise-assisted reprogrammable nanomechanical logic gate,” Nano Lett. 10, 1168–1171 (2010).
[Crossref] [PubMed]

Han, J.

Hänggi, P.

L. Gammaitoni, P. Hänggi, P. Jung, and F. Marchesoni, “Stochastic resonance,” Rev. Mod. Phys. 70, 45–105 (1998).
[Crossref]

Hartmann, F.

L. Worschech, F. Hartmann, T. Kim, S. Höfling, M. Kamp, A. Forchel, J. Ahopelto, I. Neri, A. Dari, and L. Gammaitoni, “Universal and reconfigurable logic gates in a compact three-terminal resonant tunneling diode,” Appl. Phys. Lett. 96, 042112 (2010).
[Crossref]

He, K.

K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Analysis Machine Intelligence 33, 2341 (2011).

Höfling, S.

L. Worschech, F. Hartmann, T. Kim, S. Höfling, M. Kamp, A. Forchel, J. Ahopelto, I. Neri, A. Dari, and L. Gammaitoni, “Universal and reconfigurable logic gates in a compact three-terminal resonant tunneling diode,” Appl. Phys. Lett. 96, 042112 (2010).
[Crossref]

Hu, H.

Huang, B.

Jaffe, J. S.

J. S. Jaffe, “Underwater optical imaging: the past, the present, and the prospects,” IEEE J. Oceanic Eng. 40, 683–700 (2015).
[Crossref]

Jung, P.

L. Gammaitoni, P. Hänggi, P. Jung, and F. Marchesoni, “Stochastic resonance,” Rev. Mod. Phys. 70, 45–105 (1998).
[Crossref]

Kamp, M.

L. Worschech, F. Hartmann, T. Kim, S. Höfling, M. Kamp, A. Forchel, J. Ahopelto, I. Neri, A. Dari, and L. Gammaitoni, “Universal and reconfigurable logic gates in a compact three-terminal resonant tunneling diode,” Appl. Phys. Lett. 96, 042112 (2010).
[Crossref]

Kim, T.

L. Worschech, F. Hartmann, T. Kim, S. Höfling, M. Kamp, A. Forchel, J. Ahopelto, I. Neri, A. Dari, and L. Gammaitoni, “Universal and reconfigurable logic gates in a compact three-terminal resonant tunneling diode,” Appl. Phys. Lett. 96, 042112 (2010).
[Crossref]

Kohar, V.

A. Sharma, V. Kohar, M. D. Shrimali, and S. Sinha, “Realizing logic gates with time-delayed synthetic genetic networks,” Nonlinear Dynamics 76, 431–439 (2014).
[Crossref]

Li, J.

Y. Li, H. Lu, J. Li, X. Li, Y. Li, and S. Serikawa, “Underwater image de-scattering and classification by deep neural network,” Comput. Electrical Eng. 54, 68–77 (2016).
[Crossref]

Li, X.

Y. Li, H. Lu, J. Li, X. Li, Y. Li, and S. Serikawa, “Underwater image de-scattering and classification by deep neural network,” Comput. Electrical Eng. 54, 68–77 (2016).
[Crossref]

Li, Y.

Y. Li, H. Lu, J. Li, X. Li, Y. Li, and S. Serikawa, “Underwater image de-scattering and classification by deep neural network,” Comput. Electrical Eng. 54, 68–77 (2016).
[Crossref]

Y. Li, H. Lu, J. Li, X. Li, Y. Li, and S. Serikawa, “Underwater image de-scattering and classification by deep neural network,” Comput. Electrical Eng. 54, 68–77 (2016).
[Crossref]

Liu, T.

Lu, H.

Y. Li, H. Lu, J. Li, X. Li, Y. Li, and S. Serikawa, “Underwater image de-scattering and classification by deep neural network,” Comput. Electrical Eng. 54, 68–77 (2016).
[Crossref]

Marchesoni, F.

L. Gammaitoni, P. Hänggi, P. Jung, and F. Marchesoni, “Stochastic resonance,” Rev. Mod. Phys. 70, 45–105 (1998).
[Crossref]

Mohanty, P.

D. N. Guerra, A. R. Bulsara, W. L. Ditto, S. Sinha, K. Murali, and P. Mohanty, “A noise-assisted reprogrammable nanomechanical logic gate,” Nano Lett. 10, 1168–1171 (2010).
[Crossref] [PubMed]

Murali, K.

D. N. Guerra, A. R. Bulsara, W. L. Ditto, S. Sinha, K. Murali, and P. Mohanty, “A noise-assisted reprogrammable nanomechanical logic gate,” Nano Lett. 10, 1168–1171 (2010).
[Crossref] [PubMed]

K. Murali, I. Rajamohamed, S. Sinha, W. L. Ditto, and A. R. Bulsara, “Realization of reliable and flexible logic gates using noisy nonlinear circuits,” Appl. Phys. Lett. 95, 194102 (2009).
[Crossref]

K. Murali, S. Sinha, W. L. Ditto, and A. R. Bulsara, “Reliable logic circuit elements that exploit nonlinearity in the presence of a noise floor,” Phys. Rev. Lett. 102, 194102 (2009).
[Crossref]

Neri, I.

L. Worschech, F. Hartmann, T. Kim, S. Höfling, M. Kamp, A. Forchel, J. Ahopelto, I. Neri, A. Dari, and L. Gammaitoni, “Universal and reconfigurable logic gates in a compact three-terminal resonant tunneling diode,” Appl. Phys. Lett. 96, 042112 (2010).
[Crossref]

Raimondo, S.

S. Raimondo and C. Silvia, “Underwater image processing: State of the art of restoration and image enhancement methods,” EURASIP J. Adv. Signal Processing pp. 1–15 (2010).

Rajamohamed, I.

K. Murali, I. Rajamohamed, S. Sinha, W. L. Ditto, and A. R. Bulsara, “Realization of reliable and flexible logic gates using noisy nonlinear circuits,” Appl. Phys. Lett. 95, 194102 (2009).
[Crossref]

Serikawa, S.

Y. Li, H. Lu, J. Li, X. Li, Y. Li, and S. Serikawa, “Underwater image de-scattering and classification by deep neural network,” Comput. Electrical Eng. 54, 68–77 (2016).
[Crossref]

Sharma, A.

A. Sharma, V. Kohar, M. D. Shrimali, and S. Sinha, “Realizing logic gates with time-delayed synthetic genetic networks,” Nonlinear Dynamics 76, 431–439 (2014).
[Crossref]

Shrimali, M. D.

A. Sharma, V. Kohar, M. D. Shrimali, and S. Sinha, “Realizing logic gates with time-delayed synthetic genetic networks,” Nonlinear Dynamics 76, 431–439 (2014).
[Crossref]

Silvia, C.

S. Raimondo and C. Silvia, “Underwater image processing: State of the art of restoration and image enhancement methods,” EURASIP J. Adv. Signal Processing pp. 1–15 (2010).

Sinha, S.

A. Sharma, V. Kohar, M. D. Shrimali, and S. Sinha, “Realizing logic gates with time-delayed synthetic genetic networks,” Nonlinear Dynamics 76, 431–439 (2014).
[Crossref]

D. N. Guerra, A. R. Bulsara, W. L. Ditto, S. Sinha, K. Murali, and P. Mohanty, “A noise-assisted reprogrammable nanomechanical logic gate,” Nano Lett. 10, 1168–1171 (2010).
[Crossref] [PubMed]

K. Murali, I. Rajamohamed, S. Sinha, W. L. Ditto, and A. R. Bulsara, “Realization of reliable and flexible logic gates using noisy nonlinear circuits,” Appl. Phys. Lett. 95, 194102 (2009).
[Crossref]

K. Murali, S. Sinha, W. L. Ditto, and A. R. Bulsara, “Reliable logic circuit elements that exploit nonlinearity in the presence of a noise floor,” Phys. Rev. Lett. 102, 194102 (2009).
[Crossref]

Song, A.

N. Wang and A. Song, “Parameter-induced logical stochastic resonance,” Neurocomputing 155, 80–83 (2015).
[Crossref]

N. Wang and A. Song, “Enhanced logical stochastic resonance in synthetic genetic networks,” IEEE Trans. Neural Networks Learning Systems 12, 2735–2739 (2015).

N. Wang and A. Song, “Set-reset latch logical operation induced by colored noise,” Phys. Lett. A 378, 1588–1592 (2014).
[Crossref]

Sun, F. F.

Sun, J.

K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Analysis Machine Intelligence 33, 2341 (2011).

Sutera, A.

R. Benzi, A. Sutera, and A. Vulpiani, “The mechanisim of stochastic resonance,” J. Phys. A: Mathmatical Genenral 14, 453–457 (1981).
[Crossref]

Tang, X.

K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Analysis Machine Intelligence 33, 2341 (2011).

Varshney, L. R.

H. Chen, L. R. Varshney, and P. K. Varshney, “Noise-enhanced information systems,” Proc. IEEE 102, 1607–1621 (2014).
[Crossref]

Varshney, P. K.

H. Chen, L. R. Varshney, and P. K. Varshney, “Noise-enhanced information systems,” Proc. IEEE 102, 1607–1621 (2014).
[Crossref]

Vulpiani, A.

R. Benzi, A. Sutera, and A. Vulpiani, “The mechanisim of stochastic resonance,” J. Phys. A: Mathmatical Genenral 14, 453–457 (1981).
[Crossref]

Wang, G.

Wang, J.

Wang, N.

B. Zheng, N. Wang, H. Zheng, Z. Yu, and J. Wang, “Object extraction from underwater images through logical stochastic resonance,” Opt. Lett. 41, 4967–4970 (2016).
[Crossref] [PubMed]

N. Wang and A. Song, “Enhanced logical stochastic resonance in synthetic genetic networks,” IEEE Trans. Neural Networks Learning Systems 12, 2735–2739 (2015).

N. Wang and A. Song, “Parameter-induced logical stochastic resonance,” Neurocomputing 155, 80–83 (2015).
[Crossref]

N. Wang and A. Song, “Set-reset latch logical operation induced by colored noise,” Phys. Lett. A 378, 1588–1592 (2014).
[Crossref]

Worschech, L.

L. Worschech, F. Hartmann, T. Kim, S. Höfling, M. Kamp, A. Forchel, J. Ahopelto, I. Neri, A. Dari, and L. Gammaitoni, “Universal and reconfigurable logic gates in a compact three-terminal resonant tunneling diode,” Appl. Phys. Lett. 96, 042112 (2010).
[Crossref]

Xu, W.

H. Zhang, T. Yang, W. Xu, and Y. Xu, “Effects of non-gaussian noise on logical stochastic resonance in a triple-well potential system,” Nonlinear Dynamics 76, 649–656 (2014).
[Crossref]

Xu, Y.

H. Zhang, T. Yang, W. Xu, and Y. Xu, “Effects of non-gaussian noise on logical stochastic resonance in a triple-well potential system,” Nonlinear Dynamics 76, 649–656 (2014).
[Crossref]

Yang, T.

H. Zhang, T. Yang, W. Xu, and Y. Xu, “Effects of non-gaussian noise on logical stochastic resonance in a triple-well potential system,” Nonlinear Dynamics 76, 649–656 (2014).
[Crossref]

Yu, M.

Yu, Z.

Zhang, H.

H. Zhang, T. Yang, W. Xu, and Y. Xu, “Effects of non-gaussian noise on logical stochastic resonance in a triple-well potential system,” Nonlinear Dynamics 76, 649–656 (2014).
[Crossref]

Zheng, B.

Zheng, H.

ACM Trans. Graphics (1)

R. Fattal, “Single image dehazing,” ACM Trans. Graphics 27, 72 (2008).
[Crossref]

Appl. Phys. Lett. (2)

K. Murali, I. Rajamohamed, S. Sinha, W. L. Ditto, and A. R. Bulsara, “Realization of reliable and flexible logic gates using noisy nonlinear circuits,” Appl. Phys. Lett. 95, 194102 (2009).
[Crossref]

L. Worschech, F. Hartmann, T. Kim, S. Höfling, M. Kamp, A. Forchel, J. Ahopelto, I. Neri, A. Dari, and L. Gammaitoni, “Universal and reconfigurable logic gates in a compact three-terminal resonant tunneling diode,” Appl. Phys. Lett. 96, 042112 (2010).
[Crossref]

Comput. Electrical Eng. (1)

Y. Li, H. Lu, J. Li, X. Li, Y. Li, and S. Serikawa, “Underwater image de-scattering and classification by deep neural network,” Comput. Electrical Eng. 54, 68–77 (2016).
[Crossref]

IEEE J. Oceanic Eng. (1)

J. S. Jaffe, “Underwater optical imaging: the past, the present, and the prospects,” IEEE J. Oceanic Eng. 40, 683–700 (2015).
[Crossref]

IEEE Trans. Image Process. (1)

J. Y. Chiang and Y.-C. Chen, “Underwater image enhancement by wavelength compensation and dehazing,” IEEE Trans. Image Process. 21, 1756–1769 (2012).
[Crossref]

IEEE Trans. Neural Networks Learning Systems (1)

N. Wang and A. Song, “Enhanced logical stochastic resonance in synthetic genetic networks,” IEEE Trans. Neural Networks Learning Systems 12, 2735–2739 (2015).

IEEE Trans. Pattern Analysis Machine Intelligence (1)

K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Analysis Machine Intelligence 33, 2341 (2011).

J. Opt. Soc. Am. (1)

J. Phys. A: Mathmatical Genenral (1)

R. Benzi, A. Sutera, and A. Vulpiani, “The mechanisim of stochastic resonance,” J. Phys. A: Mathmatical Genenral 14, 453–457 (1981).
[Crossref]

Nano Lett. (1)

D. N. Guerra, A. R. Bulsara, W. L. Ditto, S. Sinha, K. Murali, and P. Mohanty, “A noise-assisted reprogrammable nanomechanical logic gate,” Nano Lett. 10, 1168–1171 (2010).
[Crossref] [PubMed]

Neurocomputing (1)

N. Wang and A. Song, “Parameter-induced logical stochastic resonance,” Neurocomputing 155, 80–83 (2015).
[Crossref]

Nonlinear Dynamics (2)

H. Zhang, T. Yang, W. Xu, and Y. Xu, “Effects of non-gaussian noise on logical stochastic resonance in a triple-well potential system,” Nonlinear Dynamics 76, 649–656 (2014).
[Crossref]

A. Sharma, V. Kohar, M. D. Shrimali, and S. Sinha, “Realizing logic gates with time-delayed synthetic genetic networks,” Nonlinear Dynamics 76, 431–439 (2014).
[Crossref]

Opt. Express (1)

Opt. Lett. (2)

Phys. Lett. A (1)

N. Wang and A. Song, “Set-reset latch logical operation induced by colored noise,” Phys. Lett. A 378, 1588–1592 (2014).
[Crossref]

Phys. Rev. E (1)

T. Frank, “Delay fokker-planck equations, novikovs theorem, and boltzmann distributions as small delay approximations,” Phys. Rev. E 72, 011112 (2005).
[Crossref]

Phys. Rev. Lett. (1)

K. Murali, S. Sinha, W. L. Ditto, and A. R. Bulsara, “Reliable logic circuit elements that exploit nonlinearity in the presence of a noise floor,” Phys. Rev. Lett. 102, 194102 (2009).
[Crossref]

Proc. IEEE (1)

H. Chen, L. R. Varshney, and P. K. Varshney, “Noise-enhanced information systems,” Proc. IEEE 102, 1607–1621 (2014).
[Crossref]

Rev. Mod. Phys. (1)

L. Gammaitoni, P. Hänggi, P. Jung, and F. Marchesoni, “Stochastic resonance,” Rev. Mod. Phys. 70, 45–105 (1998).
[Crossref]

Other (1)

S. Raimondo and C. Silvia, “Underwater image processing: State of the art of restoration and image enhancement methods,” EURASIP J. Adv. Signal Processing pp. 1–15 (2010).

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Figures (4)

Fig. 1
Fig. 1

The main schematic flow of proposed method. (a) The raw image with sketched ROI window (red rectangle). (b) Selected ROI image(containing number 4). (c) Expanded selected image to 1D form, and processing each pixel of the 1D rows from the beginning parallelly. The local pixel (blue) and corresponding τstep delayed pixel (pink) are the inputs of the nonlinear bistable system. (d) Added gaussian noise. (e) Nonlinear bistable system, multilayer structure is used to realize parallel computing. (f) The output of nonlinear bistable system. (g) Final detection output.

Fig. 2
Fig. 2

(a) The effective potential of Eq. (3) for different c with τ = 40. (b)The effective potential of Eq. (3) for different time delay with c = 0.2. Other parameters of the system are set as a = 0.8, b = 1, r = 0, and the noise power D = 0 for both plots. (c) The input and output of the LSR system with delay loop. From top to bottom, panel 1 shows the input I(t), panel 2–4 show the responses of the system with τ set to be 0, 10, and 40, respectively. Other parameters of the system are set as a = 1.25, b = 1, c = 0.2, r = 0.3, and the noise power D = 0.5.

Fig. 3
Fig. 3

Badly degraded images captured in offshore seawater (left column) and the corresponding detection results obtained by our method. The images (rightmost) on the upper row of each group are the results of LSR without delay loop and the ones on the bottom row are the results with delay loop. (a) Image at distance d = 1m from camera to test object with artificial illumination. (b) Image at distance d = 1m from camera to test object with natural illumination. (c) Image at distance d = 1.3m from camera to test object with artificial illumination. The parameters of the system are set as a = 0.85, b = 1, c = 0.45, r = −0.05, and the noise power D = 0.3. τ is set to be 3, 5, and 7 from up to bottom.

Fig. 4
Fig. 4

Four groups of the cropped original image, the labeled truth-value image, probability of correct detection versus added noise power PD curve and the colormap of probability of correct detection versus added noise power and delay length PD&τ. Here, PD curve is obtained by setting τ = 10. Other parameters of the system are set as: a = 0.85, b = 1, c = 0.4, r = −0.05. The four original images are cropped from the same original picture [Fig. 3(c)].

Equations (7)

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N t d ( z , θ , ϕ ) = N t 0 ( z , θ , ϕ ) e ( α ( z ) d ) + N ( z t , θ , ϕ ) e ( K ( z , θ , ϕ ) d cos θ ) ( 1 e ( α ( z ) d + K ( z , θ , ϕ ) d cos θ ) ) .
x ˙ ( t ) = V ( x ( t ) , x ( t τ ) ) + r + I ( t ) + η ( t )
V ( x ( t ) , x ( t τ ) ) = b 4 x ( t ) 4 a 2 x ( t ) 2 c 2 x ( t τ ) 2
V eff ( x ) = ( 1 + c τ ) ( b x 4 4 ( a + c ) x 2 2 )
P o = n o n ot ,
P b = n b n bt
P = P o + ( 1 ) P b .