## 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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A. K. Jain algorithm average back-projection bandlimited binary bits block boundary called causal circulant matrix coder color convolution coordinates cosine transform covariance function data compression defined denote density Digital Image distortion DPCM edge entropy equations estimate example fast transform Figure Fourier transform frequency response Gaussian given gives gradient gray level Hadamard transform histogram IEEE Trans image processing image restoration impulse response input interpolation inverse KL transform linear low-pass filter luminance matrix mean square error method noise object obtained one-dimensional operations optimum orthogonal output pixel prediction error predictor Problem projections pseudoinverse quantizer Radon transform random field random variable reconstruction recursive region representation represents run-length sampling scan Section semicausal shown in Fig shows signal spatial spectral spectrum stationary stochastic techniques texture theorem Toeplitz Toeplitz matrix transform coding transform coefficients two-dimensional unitary DFT unitary transform values variance vector Wiener filter zero mean