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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algorithms applications average basis called causal coding coefficients color column complex considered contrast convolution coordinates cosine covariance defined definition density determined display distortion distribution elements energy entropy equal equations error estimate Example Figure filter Fourier transform frequency function Gaussian given gives Hadamard IEEE Trans image processing input interpolation inverse KL transform levels linear luminance match matrix mean square method noise noncausal obtained operations optimum orthogonal output performance periodic pixel practical prediction primary Problem processing properties quantizer random field random variable reconstruction reference region representation represents respectively response result sampling scan semicausal separable sequence shown shows signal sine sources space spatial spectral spectrum stationary Table theory Toeplitz two-dimensional uniform unitary values variance vector yields zero ΣΣ