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. |
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a₁ algorithms autocorrelation bandlimited basis images basis vectors C₁ C₂ called circulant matrix color coordinates cosine transform covariance function data compression defined density Digital eigenvalues eigenvectors energy compaction entropy equations Example fast transform Figure finite-order Fourier transform frequency Gaussian given gray level Hadamard transform IEEE Trans image enhancement image processing interpolation inverse KL transform linear low-pass filter luminance mean square error mean square quantizer noise noncausal MVR NTSC obtained one-dimensional operations optimum mean square orthogonal output pixels Problem properties random field random sequence random variable reconstruction recursive representation sampling scan Section semicausal models semicausal MVRs shown in Fig shows signal sine transform spectrum ẞ² stationary Theory Toeplitz Toeplitz matrix transform coefficients tristimulus 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