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Book Overview & Buying
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Table Of Contents
Hands-On Image Processing and Computer Vision with Python - Second Edition
By :
Hands-On Image Processing and Computer Vision with Python
By:
Overview of this book
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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Table of Contents (20 chapters)
Preface
Free Chapter
Part 1: Foundations of Digital Image Processing
Getting Started with Digital Image Processing
Image Manipulation
More Image Manipulation
Sampling and Fourier Transform
Convolution and Spatial/Frequency Domain Filtering
Frequency Domain Filtering
Part 2: Image Enhancement and Restoration Techniques
Image Enhancement
Image Enhancements Using Derivatives
Image Restoration: Inverse Problems in Imaging
Part 3: Computer Vision and Generative AI
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
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Index
(black) to 
(white), the inversion formula is as follows:
is the number of different intensity values, as in these examples:
for an 8-bit grayscale image (pixel values in 
)
for a floating-point image normalized to 
or 
) should be performed before applying the inversion formula to avoid incorrect results.
