Fundamentals of Image Processing with Python
Autor Fato& Yarman Vural, Mo&, Ertu&en Limba Engleză Hardback – 12 noi 2026
Image Processing introduces a novel approach to image processing methods, combining the foundational and deep learning approaches. It integrates neuroscientific findings with mathematical formalism and practical implementation techniques and seamlessly blends insights from neuroscience and mathematical concepts.
The book is enriched with practical Python programs, allowing readers to run and observe the output of many image processing methods, such as sampling, quantization, interpolation, filtering in spatial and transform domains, histogram operations, morphological operations, boundary extraction, object detection, and image segmentation. Readers can adjust these programs and change various parameters to observe the practical implications of the theoretical representations.
The book is organized into four abstraction levels:
- Fundamentals of image processing, including the human visual system, mathematical tools for image representation and processing, and color perception with its formal representation.
- Low-level image processing techniques in the spatial and transform domains, including point operations, histogram techniques, convolutional filters, Fourier, cosine, and Hadamard transforms, multiresolution image analysis, and wavelet transforms.
- Intermediate-level image processing techniques, including image compression, morphological image processing, image segmentation methods, such as k-means, mean-shift, and normalized cuts, as well as image representation through feature extraction (e.g., polygon approximation, Gabor and SIFT features) and whole-image representation using trees and graphs.
- High-level image processing techniques with deep learning, including Multi-Layer Perceptrons (MLPs), Artificial Neural Networks, Convolutional Neural Networks (CNNs), Autoencoders (AEs), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models (DMs), and Vision Transformers (ViTs) with their applications in image denoising, super-resolution, image colorization, image inpainting, image compression and dimensionality reduction, image segmentation, image-to-text generation, text-to-image generation, and object detection.
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Specificații
Notă biografică
Fatö Tunay Yarman Vural is a Professor in the Department of Computer Engineering at Middle East Technical University, Turkey. She is also a Senior Member of the IEEE.
Hazal Möultay Özcan is currently an Instructor in the Department of Computer Engineering at Middle East Technical University, Turkey.
It¿r Önal Ertu¿rul is an Assistant Professor with the Department of Information and Computing Science at Utrecht University, Netherlands.
Hazal Möultay Özcan is currently an Instructor in the Department of Computer Engineering at Middle East Technical University, Turkey.
It¿r Önal Ertu¿rul is an Assistant Professor with the Department of Information and Computing Science at Utrecht University, Netherlands.
Cuprins
Part I: Fundamentals
Chapter 1 Introduction
1.1 Electromagnetic Spectrum and Visible Band
1.2 Types of Images
1.3 Applications of Image Processing
1.4 Image Processing Methods and Organization of the Book
1.5 Chapter Summary
Chapter 2 Human Visual System and Image Perception
2.1 History
2.2 Human Visual System: Eye-Brain Channel
2.3 A Conceptual Model for Human Visual System
2.4 Vision Theories
2.5 Characteristics of Human Visual System
2.6 Optical Illusions
2.7 Chapter Summary
Chapter 3 Mathematical Tools for Image Processing
3.1 Image Representation
3.2 A Simple Image Formation Model
3.3 Discrete Geometry
3.4 Adjacency, Path and Connectivity
3.5 Object(s), Background, Edges and Boundaries
3.6 Sampling and Quantization
3.7 Image Interpolation
3.8 Mathematical Operations for Image Processing
3.9 Chapter Summary
Chapter 4 Color Representation
4.1 Color Generation from the Electromagnetic Spectrum
4.2 Trichromatic Theory for Color Sensation
4.3 Mathematical Representation of Color
4.4 YIQ Color Systems
4.5 YCbCr Color Systems
4.6 Conversion Between the Color Systems
4.7 Color Normalization and Chromaticity Diagrams
4.8 Color Histograms
4.9 Measuring Image Similarity by Histograms
4.10 Pseudo Coloring
4.11 Chapter Summary
Part II Low-Level Image Processing Methods
Chapter 5 Spatial Image Enhancement by Point Operations
5.1 Color Transforms
5.2 Histogram Processing
5.3 Chapter Summary
Chapter 6 Spatial Image Enhancement by Filtering: Convolution
6.1 Filtering: Extraction of Intermediate Meaning(s) from Pixel Groups
6.2 Two-Dimensional Convolution and Correlation Operations
6.3 Practical Implementation of Convolution for Image Processing
6.4 How does Convolution Work as a Filter?
6.5 Convolution as a Spatial Filter
6.6 Sharpening Filters: Edge Detectors
6.7 Chapter Summary
Chapter 7 Image Transforms
7.1 Vector Spaces for Images
7.2 Matrix Algebra for Image Processing
7.3 Fourier Transform of Images
7.4 Fourier Transform of Two-Dimensional Discrete Functions, Formulted as a Matrix Transform
7.5 Discrete Cosine Transform
7.6 Hadamard Transform
7.7 Filtering in Transform Domain
7.8 Transform Domain Filters
7.9 Chapter Summary
Chapter 8 Wavelet Transforms and Multiresolution Image Analysis
8.1 Wavelet Transforms
8.2 Image Pyramids and Trees
8.3 Sub-Band Coding
8.4 Chapter Summary
Part III Intermediate Level Image Processing Methods
Chapter 9 Image Compression
9.1 Data and Information
9.2 Measuring Information
9.3 Entropy: Average Information
9.4 Information Content of Image
9.5 Source Coding Theorem
9.6 Compression Ratio and Relevant Redundancy
9.7 Types of Redundancies in Images
9.8 Image Compression Methods
9.9 An Image Compression Model as an Information Channel
9.10 Algorithms for Reducing the Redundancies, in Source Encoder
9.11 Image File Formats
9.12 Image Fidelity
Chapter 10 Morphological Image Processing
10.1 Mathematical Morphology
10.2 Morphological Operations for Binary Images
10.3 Image Processing Techniques with Binary Morphological Operations
10.4 Binary Morphology for Color Images
10.5 Intensity-Based Morphology for Color Images
10.6 Image Processing Techniques Using Intensity-Based Morphological Operations
10.7 Popular Applications of Morphological Operations in Image Processing
10.8 Application to Remote Sensing
10.9 Chapter Summary
Chapter 11 Image Segmentation
11.1 Types of Segmentation Algorithms
11.2 Formal Definition of Image Segmentation
11.3 Clustering Techniques for Segmentation
11.4 Segmentation by Boundary Extraction
11.5 Boundary Extraction
11.6 Chapter Summary
Chapter 12 Image Representation by Feature Extraction
12.1 Region Representation by Shape Features
12.2 Region Representation with Color and Texture Features
12.3 Autocorrelation Function
12.4 Co-Occurrence Matrix
12.5 Texture Descriptors Based on Co-Occurrence Matrix
12.6 Gabor Filters
12.7 Scale Invariant Feature Transform (SIFT)
12.8 Local Binary Pajerns (LBP)
12.9 Whole Image Representation
12.10 Low Level Representations of Whole Images
12.11 High Level Representations of Whole Images
12.12 Tree Representation of Images
12.13 Graph Representation of Images
12.14 Region Adjacency Graphs
12.15 Ajributed Relational Graphs
Part IV High-Level Image Processing Methods by Deep Neural Networks
Chapter 13 Artificial Neural Networks: A Brief Overview
13.1 What is Natural Learning?
13.2 What is Artificial Learning
13.3 Types of the Learning Problem
13.4 Types of Learning Algorithms
13.5 Model Estimation by Artificial Neural Networks
13.6 Inference Algorithm
13.7 Fundamental Assumptions of Neural Network Algorithms
13.8 Experimental Set-up For Measuring the Performance
13.9 Performance Measures
13.10 Overfikng and Underfikng Problem
13.11 Chapter Summary
Chapter 14 Key Deep Learning Architectures for Image Processing Tasks
14.1 Convolutional Neural Networks
14.2 Autoencoders
14.3 Variational Autoencoders (VAEs)
14.4 Generative Adversarial Networks
14.5 Diffusion Models
14.6 Comparison of Key Deep Learning Architectures for Image Processing
14.7 Transfer Learning
14.8 Chapter Conclusion
Chapter 15 Core Image Processing Tasks with Deep Learning
15.1 Image Denoising
15.2 Image Super-Resolution
15.3 Image Colorization
15.4 Image Inpainting
15.5 Image Compression
15.6 Dimensionality Reduction
15.7 Image Generation (Synthesis)
15.8 Image-to-Image Translation
15.9 Text-to-Image Generation
15.10 Image-to-Text GAN
15.11 Image Editing GANs
15.12 Image Segmentation
15.13 Style Transfer
15.14 Challenges and Limitations
15.15 Chapter Summary
Chapter 1 Introduction
1.1 Electromagnetic Spectrum and Visible Band
1.2 Types of Images
1.3 Applications of Image Processing
1.4 Image Processing Methods and Organization of the Book
1.5 Chapter Summary
Chapter 2 Human Visual System and Image Perception
2.1 History
2.2 Human Visual System: Eye-Brain Channel
2.3 A Conceptual Model for Human Visual System
2.4 Vision Theories
2.5 Characteristics of Human Visual System
2.6 Optical Illusions
2.7 Chapter Summary
Chapter 3 Mathematical Tools for Image Processing
3.1 Image Representation
3.2 A Simple Image Formation Model
3.3 Discrete Geometry
3.4 Adjacency, Path and Connectivity
3.5 Object(s), Background, Edges and Boundaries
3.6 Sampling and Quantization
3.7 Image Interpolation
3.8 Mathematical Operations for Image Processing
3.9 Chapter Summary
Chapter 4 Color Representation
4.1 Color Generation from the Electromagnetic Spectrum
4.2 Trichromatic Theory for Color Sensation
4.3 Mathematical Representation of Color
4.4 YIQ Color Systems
4.5 YCbCr Color Systems
4.6 Conversion Between the Color Systems
4.7 Color Normalization and Chromaticity Diagrams
4.8 Color Histograms
4.9 Measuring Image Similarity by Histograms
4.10 Pseudo Coloring
4.11 Chapter Summary
Part II Low-Level Image Processing Methods
Chapter 5 Spatial Image Enhancement by Point Operations
5.1 Color Transforms
5.2 Histogram Processing
5.3 Chapter Summary
Chapter 6 Spatial Image Enhancement by Filtering: Convolution
6.1 Filtering: Extraction of Intermediate Meaning(s) from Pixel Groups
6.2 Two-Dimensional Convolution and Correlation Operations
6.3 Practical Implementation of Convolution for Image Processing
6.4 How does Convolution Work as a Filter?
6.5 Convolution as a Spatial Filter
6.6 Sharpening Filters: Edge Detectors
6.7 Chapter Summary
Chapter 7 Image Transforms
7.1 Vector Spaces for Images
7.2 Matrix Algebra for Image Processing
7.3 Fourier Transform of Images
7.4 Fourier Transform of Two-Dimensional Discrete Functions, Formulted as a Matrix Transform
7.5 Discrete Cosine Transform
7.6 Hadamard Transform
7.7 Filtering in Transform Domain
7.8 Transform Domain Filters
7.9 Chapter Summary
Chapter 8 Wavelet Transforms and Multiresolution Image Analysis
8.1 Wavelet Transforms
8.2 Image Pyramids and Trees
8.3 Sub-Band Coding
8.4 Chapter Summary
Part III Intermediate Level Image Processing Methods
Chapter 9 Image Compression
9.1 Data and Information
9.2 Measuring Information
9.3 Entropy: Average Information
9.4 Information Content of Image
9.5 Source Coding Theorem
9.6 Compression Ratio and Relevant Redundancy
9.7 Types of Redundancies in Images
9.8 Image Compression Methods
9.9 An Image Compression Model as an Information Channel
9.10 Algorithms for Reducing the Redundancies, in Source Encoder
9.11 Image File Formats
9.12 Image Fidelity
Chapter 10 Morphological Image Processing
10.1 Mathematical Morphology
10.2 Morphological Operations for Binary Images
10.3 Image Processing Techniques with Binary Morphological Operations
10.4 Binary Morphology for Color Images
10.5 Intensity-Based Morphology for Color Images
10.6 Image Processing Techniques Using Intensity-Based Morphological Operations
10.7 Popular Applications of Morphological Operations in Image Processing
10.8 Application to Remote Sensing
10.9 Chapter Summary
Chapter 11 Image Segmentation
11.1 Types of Segmentation Algorithms
11.2 Formal Definition of Image Segmentation
11.3 Clustering Techniques for Segmentation
11.4 Segmentation by Boundary Extraction
11.5 Boundary Extraction
11.6 Chapter Summary
Chapter 12 Image Representation by Feature Extraction
12.1 Region Representation by Shape Features
12.2 Region Representation with Color and Texture Features
12.3 Autocorrelation Function
12.4 Co-Occurrence Matrix
12.5 Texture Descriptors Based on Co-Occurrence Matrix
12.6 Gabor Filters
12.7 Scale Invariant Feature Transform (SIFT)
12.8 Local Binary Pajerns (LBP)
12.9 Whole Image Representation
12.10 Low Level Representations of Whole Images
12.11 High Level Representations of Whole Images
12.12 Tree Representation of Images
12.13 Graph Representation of Images
12.14 Region Adjacency Graphs
12.15 Ajributed Relational Graphs
Part IV High-Level Image Processing Methods by Deep Neural Networks
Chapter 13 Artificial Neural Networks: A Brief Overview
13.1 What is Natural Learning?
13.2 What is Artificial Learning
13.3 Types of the Learning Problem
13.4 Types of Learning Algorithms
13.5 Model Estimation by Artificial Neural Networks
13.6 Inference Algorithm
13.7 Fundamental Assumptions of Neural Network Algorithms
13.8 Experimental Set-up For Measuring the Performance
13.9 Performance Measures
13.10 Overfikng and Underfikng Problem
13.11 Chapter Summary
Chapter 14 Key Deep Learning Architectures for Image Processing Tasks
14.1 Convolutional Neural Networks
14.2 Autoencoders
14.3 Variational Autoencoders (VAEs)
14.4 Generative Adversarial Networks
14.5 Diffusion Models
14.6 Comparison of Key Deep Learning Architectures for Image Processing
14.7 Transfer Learning
14.8 Chapter Conclusion
Chapter 15 Core Image Processing Tasks with Deep Learning
15.1 Image Denoising
15.2 Image Super-Resolution
15.3 Image Colorization
15.4 Image Inpainting
15.5 Image Compression
15.6 Dimensionality Reduction
15.7 Image Generation (Synthesis)
15.8 Image-to-Image Translation
15.9 Text-to-Image Generation
15.10 Image-to-Text GAN
15.11 Image Editing GANs
15.12 Image Segmentation
15.13 Style Transfer
15.14 Challenges and Limitations
15.15 Chapter Summary