Image Enhancement by High-Order Gaussian Derivative Filters Simulating Non-classical Receptive Fields in the Human Visual System

  • Kuntal Ghosh
  • Sandip Sarkar
  • Kamales Bhaumik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)


The non-linearity exhibited by the non-classical receptive field in human visual system has been combined with the linear classical receptive field model. This enables us to construct higher order Gaussian Derivatives as a linear combination of lower order derivatives at different scales. Based on this, a new kernel which simulates non-classical receptive fields with extended disinhibitory surrounds, has been proposed. It is easy to implement and finds justification from an old psychophysical angle too. The proposed kernel has been shown to perform better than the well-known Laplacian kernel, which models the classical excitatory-inhibitory receptive fields.


Human Visual System Image Enhancement Lower Order Derivative Fourth Order Derivative Laplacian Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Marr, D., Hildreth, E.: Theory of edge detection. Proceedings of Royal Society of London B 207, 187–217 (1980)CrossRefGoogle Scholar
  2. 2.
    Rodieck, R.W.: Quantitative analysis of cat retinal ganglion cell response to visual stimuli. Vision Research 5, 583–601 (1965)CrossRefGoogle Scholar
  3. 3.
    Enroth-Cugell, C., Robson, J.G.: The contrast sensitivity of the retinal ganglion cells of the cat. Journal of Physiology (London) 187, 517–552 (1966)Google Scholar
  4. 4.
    Poggio, T., Torre, V.: Ill-posed problems and regularization analysis in early vision, vol. 773. MIT AI Memo, Cambridge (1984)Google Scholar
  5. 5.
    Poggio, T., Voorhees, H., Yuille, A.: A Regularized solution to edge detection, vol. 833. MIT AI Memo, Cambridge (1985)Google Scholar
  6. 6.
    Gonzalez, R.C., Woods, R.E.: Digital image processing, 2nd edn., Third Indian Reprint, Pearson-Education, pp. 125–131 (2003)Google Scholar
  7. 7.
    Ikeda, H., Wright, J.: Functional oganization of the periphery effect in retinal ganglion cells. Vision Research 12, 1857–1879 (1972)CrossRefGoogle Scholar
  8. 8.
    Passaglia, C.L., Enroth-Cugell, C., Troy, J.B.: Effects of remote stimulation on the mean firing rate of cat retinal ganglion cells. Journal of Neuroscience 21, 5794–5803 (2001)Google Scholar
  9. 9.
    Ghosh, K., Sarkar, S., Bhaumik, K.: A bio-inspired model for multi-scale representation of even order gaussian derivatives. In: Proceedings of International Conference on Intelligent Sensors, Sensor Networks and Information Processings (ISSNIP), vol. 994, pp. 497–502. IEEE EX, Los Alamitos (2004) ISBN: 0-7803-8893-3Google Scholar
  10. 10.
    Ma, S.D., Li, B.: Derivative computation by multiscale filters. Image and Vision Computing 16, 43–53 (1998)CrossRefGoogle Scholar
  11. 11.
    Young, R.A.: The gaussian derivative theory of spatial vision: analysis of cortical cell receptive field line weighing profiles. In: GMR, vol. 4920 (1985)Google Scholar
  12. 12.
    Koenderink, J.J., van Doorn, A.J.: Receptive field families. Biological Cybernetics 63, 291–297 (1990)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Ghosh, K., Sarkar, S., Bhaumik, K.: Low-level brightness contrast illusions and non-classical receptive field of mammalian retina. In: Proceedings of Second International Conference on Intelligent Sensing and Information Processing (ICISIP), vol. 979, pp. 529–534. IEEE EX, Los Alamitos (2005) ISBN: 0-7803-8840-2Google Scholar
  14. 14.
    Mach, E.: On the physiological effect of spatially distributed light stimuli (1986) In: Ratliff, F. (ed.) Mach Bands: Quantitative Studies On Neural Network In The Retina, Holden-Day, San Francisco, pp. 299–306 (1965)Google Scholar
  15. 15.
    Ghosh, K., Sarkar, S., Bhaumik, K.: A new mask for unsharp masking based on human visual system. In: Proceedings of National Conference on Image Processing (NCIP), IEEE Conf. Rec., vol. 10435, pp. 113–116 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Kuntal Ghosh
    • 1
  • Sandip Sarkar
    • 1
  • Kamales Bhaumik
    • 1
  1. 1.Microelectronics DivisionSaha Institute of Nuclear PhysicsKolkataIndia

Personalised recommendations