Abstract

For images, stochastic resonance or useful-noise effects have previously been assessed with low-level pixel-based information measures. Such measures are not sensitive to coherent spatial structures usually existing in images. As a result, we show that such measures are not sufficient to properly account for stochastic resonance occurring in visual perception. We introduce higher-level similarity measures, inspired from visual perception, and based on local feature descriptors of scale invariant feature transform (SIFT) type. We demonstrate that such SIFT-based measures allow for an assessment of stochastic resonance that matches the visual perception of images with spatial structures. Constructive action of noise is registered in this way with both additive noise and multiplicative speckle noise. Speckle noise, with its grainy appearance, is particularly prone to introducing spurious spatial structures in images, and the stochastic resonance visually perceived and quantitatively assessed with SIFT-based measures is specially examined in this context.

© 2012 Optical Society of America

PDF Article

References

  • View by:
  • |
  • |
  • |

  1. M. D. McDonnell, N. G. Stocks, C. E. M. Pearce, and D. Abbott, Stochastic Resonance: From Suprathreshold Stochastic Resonance to Stochastic Signal Quantization (Cambridge University, 2008).
  2. B. M. Jost and B. E. A. Saleh, “Signal-to-noise ratio improvement by stochastic resonance in a unidirectional photorefractive ring resonator,” Opt. Lett. 21, 287–289 (1996).
    [CrossRef]
  3. F. Vaudelle, J. Gazengel, G. Rivoire, X. Godivier, and F. Chapeau-Blondeau, “Stochastic resonance and noise-enhanced transmission of spatial signals in optics: the case of scattering,” J. Opt. Soc. Am. B 15, 2674–2680 (1998).
    [CrossRef]
  4. K. P. Singh, G. Ropars, M. Brunel, and A. Le Floch, “Stochastic resonance in an optical two-order parameter vectorial system,” Phys. Rev. Lett. 87, 213901 (2001).
    [CrossRef]
  5. F. Marino, M. Giudici, S. Barland, and S. Balle, “Experimental evidence of stochastic resonance in an excitable optical system,” Phys. Rev. Lett. 88, 040601 (2002).
    [CrossRef]
  6. D. V. Dylov and J. W. Fleischer, “Nonlinear self-filtering of noisy images via dynamical stochastic resonance,” Nat. Photon. 4, 323–328 (2010).
    [CrossRef]
  7. S. Blanchard, D. Rousseau, D. Gindre, and F. Chapeau-Blondeau, “Constructive action of the speckle noise in a coherent imaging system,” Opt. Lett. 32, 1983–1985 (2007).
    [CrossRef]
  8. F. Chapeau-Blondeau, D. Rousseau, S. Blanchard, and D. Gindre, “Optimizing the speckle noise for maximum efficacy of data acquisition in coherent imaging,” J. Opt. Soc. Am. A 25, 1287–1292 (2008).
    [CrossRef]
  9. E. Simonotto, M. Riani, C. Seife, M. Roberts, J. Twitty, and F. Moss, “Visual perception of stochastic resonance,” Phys. Rev. Lett. 78, 1186–1189 (1997).
    [CrossRef]
  10. D. Rousseau, A. Delahaies, and F. Chapeau-Blondeau, “Structural similarity measure to assess improvement by noise in nonlinear image transmission,” IEEE Signal Process. Lett. 17, 36–39 (2010).
    [CrossRef]
  11. Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9, 81–84 (2002).
    [CrossRef]
  12. L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Trans. Pattern Anal. Machine Intell. 20, 1254–1259 (1998).
    [CrossRef]
  13. J. Li and N. M. Allison, “A comprehensive review of current local features for computer vision,” Neurocomputing 71, 1771–1787 (2008).
    [CrossRef]
  14. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004).
    [CrossRef]
  15. http://www.vlfeat.org/∼vedaldi/code/sift.html .
  16. J. W. Goodman, Speckle Phenomena in Optics: Theory and Applications (Roberts & Company, 2006).
  17. F. Moss, L. M. Ward, and W. G. Sannita, “Stochastic resonance and sensory information processing: a tutorial and review of application,” Clin. Neurophysiol. 115, 267–281 (2004).
    [CrossRef]
  18. A. T. Duchowski, Eye Tracking Methodology: Theory and Practice (Springer, 2007).
  19. A. Delahaies, D. Rousseau, and F. Chapeau-Blondeau, “Joint acquisition-processing approach to optimize observation scales in noisy imaging,” Opt. Lett. 36, 972–974 (2011).
    [CrossRef]
  20. A. Delahaies, D. Rousseau, D. Gindre, and F. Chapeau-Blondeau, “Exploiting the speckle noise for compressive sensing,” Opt. Commun. 284, 3939–3945 (2011).
    [CrossRef]
  21. D. Ruderman and W. Bialek, “Statistics of natural images: scaling in the woods,” Phys. Rev. Lett. 73, 814–817 (1994).
    [CrossRef]
  22. P. Isola, J. Xiao, A. Torralba, and A. Oliva, “What makes an image memorable?” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2011), pp. 145–156.

2011 (2)

A. Delahaies, D. Rousseau, D. Gindre, and F. Chapeau-Blondeau, “Exploiting the speckle noise for compressive sensing,” Opt. Commun. 284, 3939–3945 (2011).
[CrossRef]

A. Delahaies, D. Rousseau, and F. Chapeau-Blondeau, “Joint acquisition-processing approach to optimize observation scales in noisy imaging,” Opt. Lett. 36, 972–974 (2011).
[CrossRef]

2010 (2)

D. V. Dylov and J. W. Fleischer, “Nonlinear self-filtering of noisy images via dynamical stochastic resonance,” Nat. Photon. 4, 323–328 (2010).
[CrossRef]

D. Rousseau, A. Delahaies, and F. Chapeau-Blondeau, “Structural similarity measure to assess improvement by noise in nonlinear image transmission,” IEEE Signal Process. Lett. 17, 36–39 (2010).
[CrossRef]

2008 (2)

2007 (1)

2004 (2)

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004).
[CrossRef]

F. Moss, L. M. Ward, and W. G. Sannita, “Stochastic resonance and sensory information processing: a tutorial and review of application,” Clin. Neurophysiol. 115, 267–281 (2004).
[CrossRef]

2002 (2)

Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9, 81–84 (2002).
[CrossRef]

F. Marino, M. Giudici, S. Barland, and S. Balle, “Experimental evidence of stochastic resonance in an excitable optical system,” Phys. Rev. Lett. 88, 040601 (2002).
[CrossRef]

2001 (1)

K. P. Singh, G. Ropars, M. Brunel, and A. Le Floch, “Stochastic resonance in an optical two-order parameter vectorial system,” Phys. Rev. Lett. 87, 213901 (2001).
[CrossRef]

1998 (2)

L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Trans. Pattern Anal. Machine Intell. 20, 1254–1259 (1998).
[CrossRef]

F. Vaudelle, J. Gazengel, G. Rivoire, X. Godivier, and F. Chapeau-Blondeau, “Stochastic resonance and noise-enhanced transmission of spatial signals in optics: the case of scattering,” J. Opt. Soc. Am. B 15, 2674–2680 (1998).
[CrossRef]

1997 (1)

E. Simonotto, M. Riani, C. Seife, M. Roberts, J. Twitty, and F. Moss, “Visual perception of stochastic resonance,” Phys. Rev. Lett. 78, 1186–1189 (1997).
[CrossRef]

1996 (1)

1994 (1)

D. Ruderman and W. Bialek, “Statistics of natural images: scaling in the woods,” Phys. Rev. Lett. 73, 814–817 (1994).
[CrossRef]

Abbott, D.

M. D. McDonnell, N. G. Stocks, C. E. M. Pearce, and D. Abbott, Stochastic Resonance: From Suprathreshold Stochastic Resonance to Stochastic Signal Quantization (Cambridge University, 2008).

Allison, N. M.

J. Li and N. M. Allison, “A comprehensive review of current local features for computer vision,” Neurocomputing 71, 1771–1787 (2008).
[CrossRef]

Balle, S.

F. Marino, M. Giudici, S. Barland, and S. Balle, “Experimental evidence of stochastic resonance in an excitable optical system,” Phys. Rev. Lett. 88, 040601 (2002).
[CrossRef]

Barland, S.

F. Marino, M. Giudici, S. Barland, and S. Balle, “Experimental evidence of stochastic resonance in an excitable optical system,” Phys. Rev. Lett. 88, 040601 (2002).
[CrossRef]

Bialek, W.

D. Ruderman and W. Bialek, “Statistics of natural images: scaling in the woods,” Phys. Rev. Lett. 73, 814–817 (1994).
[CrossRef]

Blanchard, S.

Bovik, A. C.

Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9, 81–84 (2002).
[CrossRef]

Brunel, M.

K. P. Singh, G. Ropars, M. Brunel, and A. Le Floch, “Stochastic resonance in an optical two-order parameter vectorial system,” Phys. Rev. Lett. 87, 213901 (2001).
[CrossRef]

Chapeau-Blondeau, F.

Delahaies, A.

A. Delahaies, D. Rousseau, and F. Chapeau-Blondeau, “Joint acquisition-processing approach to optimize observation scales in noisy imaging,” Opt. Lett. 36, 972–974 (2011).
[CrossRef]

A. Delahaies, D. Rousseau, D. Gindre, and F. Chapeau-Blondeau, “Exploiting the speckle noise for compressive sensing,” Opt. Commun. 284, 3939–3945 (2011).
[CrossRef]

D. Rousseau, A. Delahaies, and F. Chapeau-Blondeau, “Structural similarity measure to assess improvement by noise in nonlinear image transmission,” IEEE Signal Process. Lett. 17, 36–39 (2010).
[CrossRef]

Duchowski, A. T.

A. T. Duchowski, Eye Tracking Methodology: Theory and Practice (Springer, 2007).

Dylov, D. V.

D. V. Dylov and J. W. Fleischer, “Nonlinear self-filtering of noisy images via dynamical stochastic resonance,” Nat. Photon. 4, 323–328 (2010).
[CrossRef]

Fleischer, J. W.

D. V. Dylov and J. W. Fleischer, “Nonlinear self-filtering of noisy images via dynamical stochastic resonance,” Nat. Photon. 4, 323–328 (2010).
[CrossRef]

Gazengel, J.

Gindre, D.

Giudici, M.

F. Marino, M. Giudici, S. Barland, and S. Balle, “Experimental evidence of stochastic resonance in an excitable optical system,” Phys. Rev. Lett. 88, 040601 (2002).
[CrossRef]

Godivier, X.

Goodman, J. W.

J. W. Goodman, Speckle Phenomena in Optics: Theory and Applications (Roberts & Company, 2006).

Isola, P.

P. Isola, J. Xiao, A. Torralba, and A. Oliva, “What makes an image memorable?” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2011), pp. 145–156.

Itti, L.

L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Trans. Pattern Anal. Machine Intell. 20, 1254–1259 (1998).
[CrossRef]

Jost, B. M.

Koch, C.

L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Trans. Pattern Anal. Machine Intell. 20, 1254–1259 (1998).
[CrossRef]

Le Floch, A.

K. P. Singh, G. Ropars, M. Brunel, and A. Le Floch, “Stochastic resonance in an optical two-order parameter vectorial system,” Phys. Rev. Lett. 87, 213901 (2001).
[CrossRef]

Li, J.

J. Li and N. M. Allison, “A comprehensive review of current local features for computer vision,” Neurocomputing 71, 1771–1787 (2008).
[CrossRef]

Lowe, D. G.

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004).
[CrossRef]

Marino, F.

F. Marino, M. Giudici, S. Barland, and S. Balle, “Experimental evidence of stochastic resonance in an excitable optical system,” Phys. Rev. Lett. 88, 040601 (2002).
[CrossRef]

McDonnell, M. D.

M. D. McDonnell, N. G. Stocks, C. E. M. Pearce, and D. Abbott, Stochastic Resonance: From Suprathreshold Stochastic Resonance to Stochastic Signal Quantization (Cambridge University, 2008).

Moss, F.

F. Moss, L. M. Ward, and W. G. Sannita, “Stochastic resonance and sensory information processing: a tutorial and review of application,” Clin. Neurophysiol. 115, 267–281 (2004).
[CrossRef]

E. Simonotto, M. Riani, C. Seife, M. Roberts, J. Twitty, and F. Moss, “Visual perception of stochastic resonance,” Phys. Rev. Lett. 78, 1186–1189 (1997).
[CrossRef]

Niebur, E.

L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Trans. Pattern Anal. Machine Intell. 20, 1254–1259 (1998).
[CrossRef]

Oliva, A.

P. Isola, J. Xiao, A. Torralba, and A. Oliva, “What makes an image memorable?” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2011), pp. 145–156.

Pearce, C. E. M.

M. D. McDonnell, N. G. Stocks, C. E. M. Pearce, and D. Abbott, Stochastic Resonance: From Suprathreshold Stochastic Resonance to Stochastic Signal Quantization (Cambridge University, 2008).

Riani, M.

E. Simonotto, M. Riani, C. Seife, M. Roberts, J. Twitty, and F. Moss, “Visual perception of stochastic resonance,” Phys. Rev. Lett. 78, 1186–1189 (1997).
[CrossRef]

Rivoire, G.

Roberts, M.

E. Simonotto, M. Riani, C. Seife, M. Roberts, J. Twitty, and F. Moss, “Visual perception of stochastic resonance,” Phys. Rev. Lett. 78, 1186–1189 (1997).
[CrossRef]

Ropars, G.

K. P. Singh, G. Ropars, M. Brunel, and A. Le Floch, “Stochastic resonance in an optical two-order parameter vectorial system,” Phys. Rev. Lett. 87, 213901 (2001).
[CrossRef]

Rousseau, D.

Ruderman, D.

D. Ruderman and W. Bialek, “Statistics of natural images: scaling in the woods,” Phys. Rev. Lett. 73, 814–817 (1994).
[CrossRef]

Saleh, B. E. A.

Sannita, W. G.

F. Moss, L. M. Ward, and W. G. Sannita, “Stochastic resonance and sensory information processing: a tutorial and review of application,” Clin. Neurophysiol. 115, 267–281 (2004).
[CrossRef]

Seife, C.

E. Simonotto, M. Riani, C. Seife, M. Roberts, J. Twitty, and F. Moss, “Visual perception of stochastic resonance,” Phys. Rev. Lett. 78, 1186–1189 (1997).
[CrossRef]

Simonotto, E.

E. Simonotto, M. Riani, C. Seife, M. Roberts, J. Twitty, and F. Moss, “Visual perception of stochastic resonance,” Phys. Rev. Lett. 78, 1186–1189 (1997).
[CrossRef]

Singh, K. P.

K. P. Singh, G. Ropars, M. Brunel, and A. Le Floch, “Stochastic resonance in an optical two-order parameter vectorial system,” Phys. Rev. Lett. 87, 213901 (2001).
[CrossRef]

Stocks, N. G.

M. D. McDonnell, N. G. Stocks, C. E. M. Pearce, and D. Abbott, Stochastic Resonance: From Suprathreshold Stochastic Resonance to Stochastic Signal Quantization (Cambridge University, 2008).

Torralba, A.

P. Isola, J. Xiao, A. Torralba, and A. Oliva, “What makes an image memorable?” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2011), pp. 145–156.

Twitty, J.

E. Simonotto, M. Riani, C. Seife, M. Roberts, J. Twitty, and F. Moss, “Visual perception of stochastic resonance,” Phys. Rev. Lett. 78, 1186–1189 (1997).
[CrossRef]

Vaudelle, F.

Wang, Z.

Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9, 81–84 (2002).
[CrossRef]

Ward, L. M.

F. Moss, L. M. Ward, and W. G. Sannita, “Stochastic resonance and sensory information processing: a tutorial and review of application,” Clin. Neurophysiol. 115, 267–281 (2004).
[CrossRef]

Xiao, J.

P. Isola, J. Xiao, A. Torralba, and A. Oliva, “What makes an image memorable?” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2011), pp. 145–156.

Clin. Neurophysiol. (1)

F. Moss, L. M. Ward, and W. G. Sannita, “Stochastic resonance and sensory information processing: a tutorial and review of application,” Clin. Neurophysiol. 115, 267–281 (2004).
[CrossRef]

IEEE Signal Process. Lett. (2)

D. Rousseau, A. Delahaies, and F. Chapeau-Blondeau, “Structural similarity measure to assess improvement by noise in nonlinear image transmission,” IEEE Signal Process. Lett. 17, 36–39 (2010).
[CrossRef]

Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9, 81–84 (2002).
[CrossRef]

IEEE Trans. Pattern Anal. Machine Intell. (1)

L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Trans. Pattern Anal. Machine Intell. 20, 1254–1259 (1998).
[CrossRef]

Int. J. Comput. Vis. (1)

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004).
[CrossRef]

J. Opt. Soc. Am. A (1)

J. Opt. Soc. Am. B (1)

Nat. Photon. (1)

D. V. Dylov and J. W. Fleischer, “Nonlinear self-filtering of noisy images via dynamical stochastic resonance,” Nat. Photon. 4, 323–328 (2010).
[CrossRef]

Neurocomputing (1)

J. Li and N. M. Allison, “A comprehensive review of current local features for computer vision,” Neurocomputing 71, 1771–1787 (2008).
[CrossRef]

Opt. Commun. (1)

A. Delahaies, D. Rousseau, D. Gindre, and F. Chapeau-Blondeau, “Exploiting the speckle noise for compressive sensing,” Opt. Commun. 284, 3939–3945 (2011).
[CrossRef]

Opt. Lett. (3)

Phys. Rev. Lett. (4)

E. Simonotto, M. Riani, C. Seife, M. Roberts, J. Twitty, and F. Moss, “Visual perception of stochastic resonance,” Phys. Rev. Lett. 78, 1186–1189 (1997).
[CrossRef]

D. Ruderman and W. Bialek, “Statistics of natural images: scaling in the woods,” Phys. Rev. Lett. 73, 814–817 (1994).
[CrossRef]

K. P. Singh, G. Ropars, M. Brunel, and A. Le Floch, “Stochastic resonance in an optical two-order parameter vectorial system,” Phys. Rev. Lett. 87, 213901 (2001).
[CrossRef]

F. Marino, M. Giudici, S. Barland, and S. Balle, “Experimental evidence of stochastic resonance in an excitable optical system,” Phys. Rev. Lett. 88, 040601 (2002).
[CrossRef]

Other (5)

M. D. McDonnell, N. G. Stocks, C. E. M. Pearce, and D. Abbott, Stochastic Resonance: From Suprathreshold Stochastic Resonance to Stochastic Signal Quantization (Cambridge University, 2008).

P. Isola, J. Xiao, A. Torralba, and A. Oliva, “What makes an image memorable?” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2011), pp. 145–156.

http://www.vlfeat.org/∼vedaldi/code/sift.html .

J. W. Goodman, Speckle Phenomena in Optics: Theory and Applications (Roberts & Company, 2006).

A. T. Duchowski, Eye Tracking Methodology: Theory and Practice (Springer, 2007).

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Metrics