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 algorithm average bandlimited basis images bits C₁ called causal circulant matrix coding color compandor convolution coordinates cosine transform data compression defined density distortion entropy equations estimate example fast transform Figure Fourier transform Gaussian given gray level grid Hadamard transform IEEE Trans image processing Image Restoration impulse response interpolation inverse KL transform linear log2 low-pass filter luminance matrix mean square error mean square quantizer method noise NTSC obtained one-dimensional operations optimum mean square orthogonal output pixel Problem pseudoinverse Radon transform random field random variable reconstruction recursive region representation sampling scan Section semicausal sequence shown in Fig shows signal sine transform spatial spectral spectrum techniques Tk Ik Tk Toeplitz transform coefficients tristimulus two-dimensional uniform quantizer unitary DFT unitary matrix unitary transforms values variance Wiener filter zero mean zī¹ β² ξι σ² ΣΣ