Integrate machine learning and AI-based approaches into practical image processing with Python Engineers and researchers implementing image processing systems need methods that bridge classical techniques with modern machine learning approaches. This book delivers both traditional and modern AI-based methods and algorithms in image enhancement, restoration, segmentation, compression, and analysis. Written by an educator and researcher with more than 40 years’ experience in signal/image processing and machine learning, this reference provides theoretical and practical tools using the Python platform for a wide range of applications. The book consists of twenty chapters covering fundamental and advanced topics including two-dimensional image modeling, wavelet transform, Kalman filters, image reconstruction and computerized tomography, layered machines, linear and nonlinear autoencoders, and associative memories. Each chapter includes practical examples demonstrating real-world applications, supported by Python code, solution manuals, and presentation materials. The treatment progresses from foundational methods suitable for senior undergraduates to research-level content for graduate students and researchers. This book also covers: Fundamental supervised and unsupervised machine learning methods with specific deep learning applications for image enhancement, segmentation, feature extraction, data compression, and classificationWavelet transform and filter banks-integrated with state-of-the-art image analysis and processingAdvanced filtering techniques including Wiener and Kalman filters, and two-dimensional image modelingPython implementations via Google collab platform enabling immediate application of theoretical concepts to practical image processing problemsInstructor resources including solution manuals and presentation materials supporting adoption in digital image processing and computer vision courses Essential for professionals in industry and research labora
