The book "Fundamentals of Digital Image Processing" by Anil K. Jain provides a comprehensive overview of the field, covering various aspects from the fundamentals to advanced techniques. The content is organized into several chapters, each focusing on specific areas of digital image processing: 1. **Introduction**: Discusses the problems and applications of digital image processing, image representation and modeling, image enhancement, restoration, analysis, reconstruction from projections, and data compression. 2. **Two-Dimensional Systems and Mathematical Preliminaries**: Introduces linear systems, shift invariance, the Fourier transform, Z-transform, optical and modulation transfer functions, matrix theory, random signals, discrete random fields, spectral density functions, estimation theory, and information theory. 3. **Image Perception**: Explores light, luminance, brightness, contrast, the visual system's MTF, visibility function, monochrome and color vision models, color matching, color coordinate systems, and temporal properties of vision. 4. **Image Sampling and Quantization**: Covers image scanning, television standards, sampling theory, practical limitations in sampling and reconstruction, image quantization, and optimal quantizers. 5. **Image Transforms**: Discusses orthogonal and unitary transforms, energy conservation, decorrelation, and various transforms such as the DFT, cosine transform, sine transform, Hadamard transform, Haar transform, and KL transform. 6. **Image Representation by Stochastic Models**: Focuses on covariance and linear system models, one-dimensional causal and noncausal representations, spectral factorization, and two-dimensional spectral factorization. 7. **Image Enhancement**: Includes point operations, histogram modeling, spatial operations, transform operations, multispectral image enhancement, false color, and color image enhancement. 8. **Image Filtering and Restoration**: Covers inverse and Wiener filtering, FIR Wiener filters, Fourier domain filters, filtering using image transforms, smoothing splines, least squares filters, generalized inverse, recursive filtering, and speckle reduction. 9. **Image Analysis and Computer Vision**: Discusses spatial feature extraction, transform features, edge detection, boundary extraction, region representation, moment representation, structure, shape features, texture, image segmentation, classification techniques, and image understanding. 10. **Image Reconstruction from Projections**: Introduces transmission, reflection, and emission tomography, the Radon transform, back-projection operator, projection theorem, inverse Radon transform, and reconstruction from blurred noisy projections. 11. **Image Data Compression**: Covers predictive techniques, transform coding theory, transform coding of images, hybrid coding, interframe coding, and coding of two-tone and color images. Each chapter includes problems and a bibliography for further reference.The book "Fundamentals of Digital Image Processing" by Anil K. Jain provides a comprehensive overview of the field, covering various aspects from the fundamentals to advanced techniques. The content is organized into several chapters, each focusing on specific areas of digital image processing: 1. **Introduction**: Discusses the problems and applications of digital image processing, image representation and modeling, image enhancement, restoration, analysis, reconstruction from projections, and data compression. 2. **Two-Dimensional Systems and Mathematical Preliminaries**: Introduces linear systems, shift invariance, the Fourier transform, Z-transform, optical and modulation transfer functions, matrix theory, random signals, discrete random fields, spectral density functions, estimation theory, and information theory. 3. **Image Perception**: Explores light, luminance, brightness, contrast, the visual system's MTF, visibility function, monochrome and color vision models, color matching, color coordinate systems, and temporal properties of vision. 4. **Image Sampling and Quantization**: Covers image scanning, television standards, sampling theory, practical limitations in sampling and reconstruction, image quantization, and optimal quantizers. 5. **Image Transforms**: Discusses orthogonal and unitary transforms, energy conservation, decorrelation, and various transforms such as the DFT, cosine transform, sine transform, Hadamard transform, Haar transform, and KL transform. 6. **Image Representation by Stochastic Models**: Focuses on covariance and linear system models, one-dimensional causal and noncausal representations, spectral factorization, and two-dimensional spectral factorization. 7. **Image Enhancement**: Includes point operations, histogram modeling, spatial operations, transform operations, multispectral image enhancement, false color, and color image enhancement. 8. **Image Filtering and Restoration**: Covers inverse and Wiener filtering, FIR Wiener filters, Fourier domain filters, filtering using image transforms, smoothing splines, least squares filters, generalized inverse, recursive filtering, and speckle reduction. 9. **Image Analysis and Computer Vision**: Discusses spatial feature extraction, transform features, edge detection, boundary extraction, region representation, moment representation, structure, shape features, texture, image segmentation, classification techniques, and image understanding. 10. **Image Reconstruction from Projections**: Introduces transmission, reflection, and emission tomography, the Radon transform, back-projection operator, projection theorem, inverse Radon transform, and reconstruction from blurred noisy projections. 11. **Image Data Compression**: Covers predictive techniques, transform coding theory, transform coding of images, hybrid coding, interframe coding, and coding of two-tone and color images. Each chapter includes problems and a bibliography for further reference.