Digital Image Processing: Theory, Practice, and AI Applications

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,