Fundamentals of Digital Image ProcessingPresents a thorough overview of the major topics of digital image processing, beginning with the basic mathematical tools needed for the subject. Includes a comprehensive chapter on stochastic models for digital image processing. Covers aspects of image representation including luminance, color, spatial and temporal properties of vision, and digitization. Explores various image processing techniques. Discusses algorithm development (software/firmware) for image transforms, enhancement, reconstruction, and image coding. |
Contents
IMAGE FILTERING AND RESTORATION | 8 |
TWODIMENSIONAL SYSTEMS AND MATHEMATICAL | 11 |
13 | 41 |
Copyright | |
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a₁ a₂ algorithms autocorrelation average bandlimited basis images basis vectors C₁ C₂ called causal chromaticity circulant matrix color convolution coordinate system cosine transform covariance function defined density display eigenvectors entropy equations Example fast transform Figure Fourier transform Gaussian given gray level H₁ Haar Hadamard transform IEEE Trans image processing impulse response interpolation inverse KL transform Kronecker product linear low-pass filter luminance mean square error mean square quantizer noise noncausal MVR NTSC obtained one-dimensional optimum mean square orthogonal output pixel Problem properties quantizer random field random sequence random variable reconstruction recursive reference white representation represents scan shown in Fig shows signal sine transform spectral spectrum stationary theory Toeplitz Toeplitz matrix transform coefficients tristimulus values two-dimensional uniform quantizer unitary DFT unitary matrix unitary transforms variance w₁ w₂ Wiener filter z₁ zero mean zz¹ Σ Σ σ² ΣΣ