Explore the world of image processing, computer vision, and generative AI with Python—from fundamental concepts and classical methods to deep learning, modern vision systems, and real-world visual content generation.
Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*
Key Features
- Master end-to-end image processing and computer vision workflows using Python
- Build visual AI systems with classical, deep learning, and generative AI techniques
- Apply theory with production-ready implementations using leading Python libraries
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
Analyzing and understanding visual data has become essential in modern applications such as healthcare, security, remote sensing, manufacturing, and digital media. This book provides a hands-on guide to image processing and computer vision using Python, following a practical approach that bridges theory with implementation.
As you progress through the chapters, you will develop proficiency in Python 3 and implement algorithms spanning classical image processing, modern computer vision, and state-of-the-art (SOTA) deep learning and generative AI. The book covers image enhancement, restoration, filtering, segmentation, feature extraction, classification, and object detection using libraries including NumPy, OpenCV, PIL, SciPy, scikit-image, scikit-learn, TensorFlow, Keras, and PyTorch.
Advanced chapters introduce CNNs, Vision Transformers, transformer-based segmentation, modern detection frameworks, GANs, diffusion models, foundation models, image-to-image translation, super-resolution, and multimodal vision-language understanding. Real-world applications span medical imaging, remote sensing, banking, augmented reality, autonomous driving, industrial inspection, and intelligent visual analytics. By the end of the book, you will be equipped to design and implement real-world visual computing solutions.
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What you will learn
- Build image processing and computer vision pipelines
- Apply image enhancement, restoration, and segmentation
- Implement image classification and object detection models
- Explore CNNs, Vision Transformers, and attention models
- Generate and edit images using GANs and diffusion models
- Develop multimodal vision-language AI applications
- Apply visual AI across diverse real-world domains
- Implement super-resolution, style transfer, and image-to-image translation
Who this book is for
Python developers, engineers, applied researchers, students, and AI practitioners who want to build end-to-end image processing and computer vision systems. A working knowledge of Python is required, while familiarity with linear algebra, calculus, and basic machine learning concepts will help you get the most from the advanced topics.
Table of Contents
- Getting Started with Digital Image Processing
- Image Manipulation
- More Image Manipulation
- Sampling and Fourier Transform
- Convolution and Spatial/Frequency Domain Filtering
- Frequency Domain Filtering
- Image Enhancement
- Image Enhancements Using Derivatives
- Image Restoration: Inverse Problems in Imaging
- Image Segmentation: From Classical Methods to Deep Learning
- More Deep Learning Methods for Image Segmentation
- Image Classification and Object Detection
- Generative AI in Image Processing and Computer Vision
Sandipan Dey is a Data Scientist with a wide range of interests in related areas, including machine learning, computer vision, image processing, and deep learning. He has been working in several areas of applied machine learning for the past few years. Sandipan earned his MS in Computer Science from UMBC and his BE in Computer Science from JU. He has published papers in several international data mining conferences and journals. He has authored several books on image processing with Python. He has also completed online specializations in machine learning, image processing, AI, and related areas through MOOCs.
