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. |
Other editions - View all
Common terms and phrases
a₁ a₂ algorithms autocorrelation average bandlimited basis images basis vectors C₁ C₂ called causal chromaticity circulant matrix color cosine transform covariance function defined density display distortion eigenvalues eigenvectors energy compaction entropy equations Example fast transform Figure first-order hold Fourier transform frequency Gaussian given gray level H₁ H₂ Hadamard transform IEEE Trans image processing input interpolation inverse KL transform Kronecker product linear low-pass filter luminance mean square error mean square quantizer noise noncausal MVR NTSC obtained one-dimensional operations optimum mean square orthogonal output pixel Problem properties random field random sequence random variable recursive representation sampling scan semicausal shown in Fig shows signal sine transform spatial spectral spectrum stationary theory Toeplitz Toeplitz matrix transform coefficients tristimulus tristimulus values two-dimensional uniform quantizer unitary DFT unitary matrix unitary transforms variance w₂ Wiener filter z₁ zero mean Σ Σ ΣΣ