Anil K. Jain's book provides a comprehensive overview of digital image processing, covering topics such as image representation, enhancement, restoration, analysis, reconstruction, and compression. The book is structured into 11 chapters, each focusing on a specific area of image processing. Chapter 1 introduces the field and its applications, while Chapter 2 provides mathematical preliminaries, including linear systems, Fourier and Z-transforms, matrix theory, and random signals. Chapter 3 discusses image perception, including light, brightness, color representation, and color vision models. Chapter 4 covers image sampling and quantization, including the Nyquist rate, aliasing, and optimal quantization techniques. Chapter 5 explores image transforms, including the DFT, cosine, sine, and KL transforms. Chapter 6 presents stochastic models for image representation, including AR and MA models. Chapter 7 focuses on image enhancement techniques such as point operations, histogram modeling, and spatial operations. Chapter 8 discusses image filtering and restoration, including inverse and Wiener filtering, recursive filtering, and blind deconvolution. Chapter 9 covers image analysis and computer vision, including feature extraction, edge detection, boundary representation, and region representation. Chapter 10 addresses image reconstruction from projections, including the Radon transform and back-projection methods. Chapter 11 discusses image data compression, including pixel coding, predictive techniques, transform coding, and hybrid coding. The book also includes a detailed index. The content is well-organized and provides a thorough foundation in digital image processing, making it a valuable resource for students and professionals in the field.Anil K. Jain's book provides a comprehensive overview of digital image processing, covering topics such as image representation, enhancement, restoration, analysis, reconstruction, and compression. The book is structured into 11 chapters, each focusing on a specific area of image processing. Chapter 1 introduces the field and its applications, while Chapter 2 provides mathematical preliminaries, including linear systems, Fourier and Z-transforms, matrix theory, and random signals. Chapter 3 discusses image perception, including light, brightness, color representation, and color vision models. Chapter 4 covers image sampling and quantization, including the Nyquist rate, aliasing, and optimal quantization techniques. Chapter 5 explores image transforms, including the DFT, cosine, sine, and KL transforms. Chapter 6 presents stochastic models for image representation, including AR and MA models. Chapter 7 focuses on image enhancement techniques such as point operations, histogram modeling, and spatial operations. Chapter 8 discusses image filtering and restoration, including inverse and Wiener filtering, recursive filtering, and blind deconvolution. Chapter 9 covers image analysis and computer vision, including feature extraction, edge detection, boundary representation, and region representation. Chapter 10 addresses image reconstruction from projections, including the Radon transform and back-projection methods. Chapter 11 discusses image data compression, including pixel coding, predictive techniques, transform coding, and hybrid coding. The book also includes a detailed index. The content is well-organized and provides a thorough foundation in digital image processing, making it a valuable resource for students and professionals in the field.