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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algorithms applications average basis bits called causal coding coefficients color column complex considered convolution coordinates cosine covariance defined density determined Digital display elements energy enhancement 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 matrix mean square method noise noncausal observation obtained operations optimum original orthogonal output performance pixels prediction Problem processing properties quantizer random field random variable realization reconstruction recursive region representation represents requires respectively response restoration result sampling scan semicausal separable sequence shown shows signal sine spatial spectral spectrum stationary Table techniques theory two-dimensional uniform unitary unitary DFT unitary transforms values variance vector Wiener filter yields zero ΣΣ