Image Formation

  • I. Pitas
  • A. N. Venetsanopoulos
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 84)


An image is a reproduction of a person or a thing, and image formation is the reproduction process. Thus, images are representations of objects, which are sensed through their radiant energy, e.g., light. Therefore, by its definition, image formation requires a radiant source,an object, and a formation system. Radiant sources can be of various kinds (e.g., white light sources, laser systems, X-ray tubes, thermal sources, even acoustic wave sources). Therefore, the physics of image formation can vary accordingly. The nature of the radiation also greatly influences the structure of the formation system. There exist formation systems which are biological (e.g., the vision system of the human and the animals), photochemical (e.g., photographic cameras) or photoelectronic (e.g., TV cameras). Thus it is very difficult to build an image formation model that can encompass this enormous variety of radiation sources and image formation systems. The model described in Figure 3.1.1 is quite general and can be used in various digital image processing and computer vision applications.


Radiant Energy Human Visual System Image Formation Digital Image Processing Impulsive Noise 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    M.D. Levine, Vision in man and machine, McGraw Hill, 1985.Google Scholar
  2. [2]
    H.0 Andrews, B.R. Hunt, Digital image restoration, Prentice-Hall, 1977.Google Scholar
  3. [3]
    J. Foley, A. Van Dam, Fundamentals of interactive computer graphics, Addison-Wesley, 1981.Google Scholar
  4. [4]
    T. E. Jenkins, Optical sensing techniques and signal processing, Prentice-Hall, 1987.Google Scholar
  5. [5]
    D.F. Barbe, “Imaging devices using the charge coupled concept”, Proceeding of the IEEE, vol. 63, pp. 38–67, 1975.CrossRefGoogle Scholar
  6. [6]
    C.E.K. Mees, The theory of the photographic process, McMillan Company, 1954.Google Scholar
  7. [7]
    D.G. Falconer, “Image enhancement and film-grain noise”, Optica Acta, vol. 17, pp. 693–705, 1970.Google Scholar
  8. [8]
    D.E. Dudgeon, R.M. Mersereau, Multidimensional digital signal processing, Prentice-Hall, 1984.Google Scholar
  9. [9]
    W.K. Pratt, Digital image processing, J. Wiley, 1978.Google Scholar
  10. [10]
    G. Westheimer, F.W. Campel, “Light distribution in the image formed by the living human eye”, Journal of the Optical Society of America, vol. 52, No. 9, pp. 1040–1045, 1962.CrossRefGoogle Scholar
  11. [11]
    G. Wizecki, W.S. Stiles, Color science, concepts and methods, quantitative data and formulas, Wiley, 1967.Google Scholar
  12. [12]
    W.F. Schreiber, “Image processing for quality improvement”, Proc. of IEEE, vol. 66, no. 12, pp. 1640–1651, Dec. 1978.Google Scholar
  13. [13]
    J.L. Mannos, D. Sacrison, “The effects of the visual fidelity criterion on the encoding of images” IEEE Transactions on Information Theory, vol. IT-20, pp. 525–536, 1974.Google Scholar
  14. [14]
    C.F. Hall, E.L. Hall, “A nonlinear model for the spatial characteristics of the human visual system”, IEEE Transactions on Systems, Man and Cybernetics, vol. SMC-7, no. 3, pp. 161–170, March 1977.CrossRefGoogle Scholar
  15. [15]
    J. Metzler (editor), Systems neuroscience, Academic Press, 1977.Google Scholar
  16. [16]
    T.G. Stockham Jr., “Image processing in the context of a visual model”, Proc. of IEEE, vol. 60, no. 7, pp. 828–842, July 1972.CrossRefGoogle Scholar
  17. [17]
    C.F Hall, “Subjective evaluation of a perceptual quality metric”, Image Quality, Proc. Soc. Photo Opt. Instr. Eng., vol. 310, pp. 200–204, 1981.Google Scholar
  18. [18]
    H. Marmolin, “Subjective MSE measures”, IEEE Transactions on Systems, Man, Machine and Cybernetics, vol. SMC-16, no. 3, pp. 486–489, 1986.Google Scholar
  19. [19]
    L. Mandel, “Fluctuations of photon beams, the distribution of the photoelectrons”, Proc. Phys. Soc. of London, vol. 74, pp. 233–243, 1959.CrossRefGoogle Scholar
  20. [20]
    C.F. Hall, “The application of human visual system models to digital color image compression”, Proc. IEEE Int. Comm. Conf., pp. 436–441, Boston, 1983.Google Scholar
  21. [21]
    B.R. Hunt, “Digital image processing”, Proc. IEEE, vol. 63. no. 4, pp. 693–708, April 1975.CrossRefGoogle Scholar
  22. [22]
    A. Papoulis, Probability, random variables and stochastic processes, McGraw-Hill, 1984.Google Scholar

Copyright information

© Springer Science+Business Media New York 1990

Authors and Affiliations

  • I. Pitas
    • 1
  • A. N. Venetsanopoulos
    • 2
  1. 1.Aristotelian University of ThessalonikiGreece
  2. 2.University of TorontoCanada

Personalised recommendations