Fundamentals of Digital Image Processing by Anil K. Jain

By Anil K. Jain

Provides an intensive assessment of the key themes of electronic snapshot processing, starting with the fundamental mathematical instruments wanted for the topic. incorporates a finished bankruptcy on stochastic types for electronic snapshot processing. Covers features of snapshot illustration together with luminance, colour, spatial and temporal houses of imaginative and prescient, and digitization. Explores quite a few photo processing options. Discusses set of rules improvement (software/firmware) for photo transforms, enhancement, reconstruction, and photograph coding.

Table of Contents
1. Dimensional structures and Mathematical Preliminaries.
2. snapshot Perception.
three. photo Sampling and Quantization.
four. picture Transforms.
five. picture illustration through Stochastic Models.
6. photograph Enhancement.
7. photograph Filtering and Restoration.
eight. picture research and laptop Vision.
nine. photograph Reconstruction From Projections.
10. photograph facts Compression.

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In the overlapping regions gives the result at (m, n). e. ,h (x - x ', y - y ')f(x ', y ')dx ' dy ' y (m, n) = h (m, n) © x (m, n) � LL h (m - m ', n - n ')x (m ', n ' ) ' ' mn = -xi (2. 11) [ill] �. 2 Examples of PSFs (a) Circularly symmetric PSF of average atmospheric turbulence causing small blur; (b) atmospheric turbulence PSF causing large blur; (c) separable PSF of a diffraction limited system with square aperature; (d) same as (c) but with smaller aperture. Figure 14 Two-Dimensional Systems and Mathematical Preliminaries Chap.

Computer Techniques in Image Processing. New York: Academic Press, 1970. 2. H. C. Andrews and B. R. Hunt. Digital Image Restoration. J. : Prentice-Hall, 1977. 3. K. R. Castleman. Digital Image Processing. J . : Prentice-Hall, 1979. 4. P. ). Digital Image Processing Techniques. New York: Academic Press, 1984. 5. R. C. Gonzalez and P. Wintz. Digital Image Processing, 2nd ed. Reading, Mass. : Addison-Wesley, 1987. 6 .. E. L. Hall. Computer Image Processing and Recognition. New York: Academic Press, 1979.

Y (N)), for any g (y) = g E [(x - i)g (y )] = 0 (2. 105) To prove this we write E [xg (y)J = E ( E (x ! y)g (y )] = E [ E (xg (y )jy )] = E [xg (y )J which implies (2. 105). Since x is a function of y, this also implies E [(x - i)i] = o (2. 106) E [(x - i)g (i)] = 0 (2. e. , the estimation error is orthogonal to every function of the estimate. The orthogonality principle has been found to be very useful in linear estima­ tion. In general, the conditional mean is a nonlinear function and is difficult to evaluate.

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