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 | 7 |
TWODIMENSIONAL SYSTEMS AND MATHEMATICAL | 11 |
2 | 13 |
Copyright | |
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a₁ a₂ algorithms autocorrelation bandlimited basis images basis vectors called causal MVR circulant matrix color convolution coordinates cosine transform covariance function defined density eigenvectors entropy equations Example fast transform Figure finite-order Fourier transform Gaussian given gray levels H₁ Haar Hadamard transform IEEE Trans image enhancement image processing image restoration impulse response interpolation inverse KL transform linear low-pass filter luminance mean square error mean square quantizer noncausal MVR NTSC obtained one-dimensional operations optimum mean square orthogonal output pixels Problem properties quantizer random field random sequence random variable reconstruction recursive representation sampling scan semicausal models semicausal MVRs shown in Fig shows signal sine transform spectrum ẞ² stationary Toeplitz Toeplitz matrix transform coefficients two-dimensional uniform quantizer unitary DFT unitary matrix unitary transforms values variance w₁ w₂ Wiener filter z₁ zero mean zī¹ zz¹ Σ Σ σ² ΣΣ