Preface

Image processing and computer vision have become foundational technologies in modern artificial intelligence, enabling machines not only to enhance and analyze images but also to interpret, understand, and generate visual content. Today, these techniques are widely used across healthcare, autonomous systems, robotics, remote sensing, scientific imaging, manufacturing, and creative media.

This book presents a hands-on and mathematically grounded journey from the fundamentals of digital image processing to state-of-the-art advances in computer vision and generative AI using Python. It begins with core concepts such as image representation, sampling, quantization, Fourier analysis, convolution, and spatial and frequency-domain filtering. Building on these foundations, it explores image enhancement, derivative-based processing, and classical image restoration techniques for solving real-world imaging problems.

The book then transitions into modern computer vision paradigms, including image segmentation, image classification, and object detection, covering both classical approaches and deep learning-based methods. Readers gain practical experience with widely used architectures and frameworks, along with applications in domains such as medical imaging, remote sensing, industrial inspection, and scene understanding.

The final chapter introduces cutting-edge generative AI methods that are redefining computer vision. Topics include GANs, VAEs, diffusion models, Stable Diffusion, ControlNet, DALL·E, GPT-based image systems, and multimodal vision-language models. These approaches illustrate the evolution of vision systems from passive perception to active generation, editing, restoration, and reasoning over visual data.

A central philosophy of this book is learning from first principles. Each topic is developed through rigorous mathematical foundations, followed by intuitive explanations and practical Python implementations. This integration of theory, intuition, and hands-on coding is designed to help readers understand not only how these methods work but also why they work.

In this second edition, the content has been extensively revised and expanded to reflect rapid advances in deep learning, multimodal AI, and generative modeling. New material incorporates recent state-of-the-art methods while maintaining a strong emphasis on mathematical rigor and practical implementation.

Python libraries evolve rapidly, and functions, classes, or APIs may be deprecated, renamed, or removed across versions. Moreover, because many libraries are developed independently by different teams, changes in one package can introduce incompatibilities with others and break dependency chains. Consequently, version mismatches may require package pinning, dependency updates, or minor code modifications to ensure seamless execution of the examples presented in this chapter.

Readers are encouraged to consult the accompanying GitHub repository for the latest code updates and supplementary materials.

Whether you are a student, researcher, engineer, or practitioner, this book provides a comprehensive pathway from classical image processing to modern computer vision and the latest generative AI systems. Follow https://youtube.com/@sandipanumbc for more videos and tutorials on these topics.