Hands-On Image Processing with Python: Advanced methods for analyzing, transforming, and interpreting digital images
Explore the world of image processing and CV (computer vision) with Python from fundamental concepts to deep learning

Key Features

  • Get to grips with image enhancement, restoration, segmentation, feature-extraction, classification, object detection, and image synthesis
  • Benefit from hands-on guidance for vital image processing tasks using Python libraries
  • Familiarize yourself with various techniques, spanning classical, machine learning, and cutting-edge deep learning methods
  • Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Being able to process, extract information, and understand an image quickly has become a critical part of real-world applications within security, payment, healthcare, advertising, and other sectors. This book covers a wide range of topics, following a highly practical approach that takes you through a set of image processing concepts and algorithms to help you learn how to use leading Python library functions to implement these algorithms. As you progress, you’ll gain proficiency in writing code snippets in Python 3 and quickly implement complex image processing and computer vision algorithms to solve problems in image enhancement, restoration, denoising, segmentation, classification, object detection and more, using image processing libraries such as PIL, scikit-mage, scipy ndimage, and opencv-python. You’ll also learn how to use ML models using scikit-learn and explore recent advances with deep learning models with CNNs (e.g., ResNet, Yolo) using tensorflow, keras, and pytorch. The final set of chapters will help you solve advanced problems, such as image-to-image translation, anisotropic diffusion, and generative arts. By the end of this book, you will be well versed in image processing and ready to solve a variety of commonly occurring problems.

What you will learn

  • Gain expertise in tackling digital image processing problems from diverse perspectives
  • Explore the intricacies of image transformation, enhancement, spatial and frequency domain filters, and morphological image processing
  • Dive into the realm of image restoration and inpainting
  • Harness the power of ML models for image classification and object detection
  • Embark on a journey into the realm of deep learning, including CNNs, attention mechanisms, autoencoders, GANs, and transfer learning
  • Push the boundaries with image captioning, pseudo coloring, 3D image processing, generative art, and quantum image processing
  • Apply your knowledge to real-world domains, including medical imaging, security systems, and remote sensing applications

Who this book is for

This book is for engineers and applied researchers interested in CV, image processing, ML and deep learning, especially those who are proficient in Python programming and want to learn how to implement different algorithms for image processing. Working knowledge of Python is a must.

1144720024
Hands-On Image Processing with Python: Advanced methods for analyzing, transforming, and interpreting digital images
Explore the world of image processing and CV (computer vision) with Python from fundamental concepts to deep learning

Key Features

  • Get to grips with image enhancement, restoration, segmentation, feature-extraction, classification, object detection, and image synthesis
  • Benefit from hands-on guidance for vital image processing tasks using Python libraries
  • Familiarize yourself with various techniques, spanning classical, machine learning, and cutting-edge deep learning methods
  • Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Being able to process, extract information, and understand an image quickly has become a critical part of real-world applications within security, payment, healthcare, advertising, and other sectors. This book covers a wide range of topics, following a highly practical approach that takes you through a set of image processing concepts and algorithms to help you learn how to use leading Python library functions to implement these algorithms. As you progress, you’ll gain proficiency in writing code snippets in Python 3 and quickly implement complex image processing and computer vision algorithms to solve problems in image enhancement, restoration, denoising, segmentation, classification, object detection and more, using image processing libraries such as PIL, scikit-mage, scipy ndimage, and opencv-python. You’ll also learn how to use ML models using scikit-learn and explore recent advances with deep learning models with CNNs (e.g., ResNet, Yolo) using tensorflow, keras, and pytorch. The final set of chapters will help you solve advanced problems, such as image-to-image translation, anisotropic diffusion, and generative arts. By the end of this book, you will be well versed in image processing and ready to solve a variety of commonly occurring problems.

What you will learn

  • Gain expertise in tackling digital image processing problems from diverse perspectives
  • Explore the intricacies of image transformation, enhancement, spatial and frequency domain filters, and morphological image processing
  • Dive into the realm of image restoration and inpainting
  • Harness the power of ML models for image classification and object detection
  • Embark on a journey into the realm of deep learning, including CNNs, attention mechanisms, autoencoders, GANs, and transfer learning
  • Push the boundaries with image captioning, pseudo coloring, 3D image processing, generative art, and quantum image processing
  • Apply your knowledge to real-world domains, including medical imaging, security systems, and remote sensing applications

Who this book is for

This book is for engineers and applied researchers interested in CV, image processing, ML and deep learning, especially those who are proficient in Python programming and want to learn how to implement different algorithms for image processing. Working knowledge of Python is a must.

49.99 Pre Order
Hands-On Image Processing with Python: Advanced methods for analyzing, transforming, and interpreting digital images

Hands-On Image Processing with Python: Advanced methods for analyzing, transforming, and interpreting digital images

by Sandipan Dey
Hands-On Image Processing with Python: Advanced methods for analyzing, transforming, and interpreting digital images

Hands-On Image Processing with Python: Advanced methods for analyzing, transforming, and interpreting digital images

by Sandipan Dey

Paperback

$49.99 
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Overview

Explore the world of image processing and CV (computer vision) with Python from fundamental concepts to deep learning

Key Features

  • Get to grips with image enhancement, restoration, segmentation, feature-extraction, classification, object detection, and image synthesis
  • Benefit from hands-on guidance for vital image processing tasks using Python libraries
  • Familiarize yourself with various techniques, spanning classical, machine learning, and cutting-edge deep learning methods
  • Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Being able to process, extract information, and understand an image quickly has become a critical part of real-world applications within security, payment, healthcare, advertising, and other sectors. This book covers a wide range of topics, following a highly practical approach that takes you through a set of image processing concepts and algorithms to help you learn how to use leading Python library functions to implement these algorithms. As you progress, you’ll gain proficiency in writing code snippets in Python 3 and quickly implement complex image processing and computer vision algorithms to solve problems in image enhancement, restoration, denoising, segmentation, classification, object detection and more, using image processing libraries such as PIL, scikit-mage, scipy ndimage, and opencv-python. You’ll also learn how to use ML models using scikit-learn and explore recent advances with deep learning models with CNNs (e.g., ResNet, Yolo) using tensorflow, keras, and pytorch. The final set of chapters will help you solve advanced problems, such as image-to-image translation, anisotropic diffusion, and generative arts. By the end of this book, you will be well versed in image processing and ready to solve a variety of commonly occurring problems.

What you will learn

  • Gain expertise in tackling digital image processing problems from diverse perspectives
  • Explore the intricacies of image transformation, enhancement, spatial and frequency domain filters, and morphological image processing
  • Dive into the realm of image restoration and inpainting
  • Harness the power of ML models for image classification and object detection
  • Embark on a journey into the realm of deep learning, including CNNs, attention mechanisms, autoencoders, GANs, and transfer learning
  • Push the boundaries with image captioning, pseudo coloring, 3D image processing, generative art, and quantum image processing
  • Apply your knowledge to real-world domains, including medical imaging, security systems, and remote sensing applications

Who this book is for

This book is for engineers and applied researchers interested in CV, image processing, ML and deep learning, especially those who are proficient in Python programming and want to learn how to implement different algorithms for image processing. Working knowledge of Python is a must.


Product Details

ISBN-13: 9781837636235
Publisher: Packt Publishing
Publication date: 01/09/2026
Product dimensions: 75.00(w) x 92.50(h) x (d)

About the Author

Sandipan Dey is a data scientist with a wide range of interests, covering topics such as machine learning, deep learning, image processing, and computer vision. He has worked in numerous data science fields, working with recommender systems, predictive models for the events industry, sensor localization models, sentiment analysis, and device prognostics. He earned his master's degree in computer science from the University of Maryland, Baltimore County, and has published in a few IEEE Data Mining conferences and journals. He has earned certifications from 100+ MOOCs on data science, machine learning, deep learning, image processing, and related courses. He is a regular blogger (sandipanweb) and is a machine learning education enthusiast.

Table of Contents

Table of Contents

  1. Getting started with Image Processing
  2. More Image Manipulation
  3. Sampling and Fourier Transform
  4. Image Enhancement
  5. Image Enhancements using Derivatives
  6. Morphological Image Processin
  7. Extracting Image Features and Descriptors
  8. Image Segmentation
  9. Classical Machine Learning Methods
  10. Deep Learning in Image Processing - Image Classification with Deep Neural Networks
  11. Object Detection, Deep Segmentation and Transfer Learning
  12. Additional Problems in Image Processing
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