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Digital Image Processing: Part I

Digital Image Processing: Part I
4,7 (14 Bewertungen)
ISBN: 978-87-7681-541-7
1. Auflage
Seiten : 71
Preis: Kostenlos

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Über das Buch

  1. Beschreibung
  2. Einleitung
  3. Inhalt


This book (vol.1 and vol.2) introduces the fundamental theories of modern digital image processing including intensity transformations, filtering in the frequency and spatial domain, restoration, colour processing, morphological operations, and segmentation. It aims to help the students, scientists, and practitioners to understand the concepts through illustrations and examples


Digital image processing is an important research area. The techniques developed in this area so far require to be summarized in an appropriate way. In this book, the fundamental theories of these techniques will be introduced. Particularly, their applications in image denoising, restoration, and segmentation will be introduced. The entire book consists of four chapters, which will be subsequently introduced.

In Chapter 1, basic concepts in digital image processing are described. Chapter 2 will see the details of image transform and spatial filtering schemes. Chapter 3 introduces the filtering strategies in the frequency domain, followed by a review of image restoration approaches described in Chapter 4.



1. Introduction
1.1 Digital image processing
1.2 Purpose of digital image processing
1.3 Application areas that use digital image processing
1.4 Components of an image processing system
1.5 Visual perception
1.6 Image acquisition
1.7 Image sampling and quantization
1.7.1 Basic concepts in sampling and quantization
1.7.2 Representing digital images
1.8 Basic relationship between pixels
1.8.1 Neighbors of a pixel
1.8.2. Adjacency, connectivity, regions, and boundaries
1.8.3 Distance measures

2. Intensity transformations and spatial filtering
2.1 Preliminaries
2.2 Basic Intensity Transformation Functions
2.3 Histogram Processing
2.3.1 Histogram Equalization
2.3.2 Histogram Matching
2.4 Fundamentals of Spatial Filtering
2.5 Smoothing Spatial Filters
2.5.1 Smoothing Linear Filters
2.5.2 Order-Statistic Filters
2.6 Sharpening filters
2.7 Combining image enhancement methods

3. Filtering in the Frequency Domain
3.1 Background
3.2 Preliminaries
3.2.1 Impulse function and its properties
3.3 Sampling and the Fourier Transform of Sampled Functions
3.3.1 Sampling
3.3.2 The Fourier Transform of Sampled Functions
3.3.3 The Sampling Theorem
3.4 Discrete Fourier Transform
3.4.1 One Dimensional DFT
3.4.2 Relationship between the Sampling and Frequency Intervals
3.5 Extension to Functions of Two Variables
3.6 Some Properties of the 2-D Discrete Fourier Transform
3.7 The Basics of Filtering in the Frequency Domain
3.8 Image Smoothing Using Frequency Domain Filters
3.8.1 Ideal Lowpass Filters
3.8.2 Butterworth Lowpass Filter
3.8.3 Gaussian Lowpass Filters
3.9 Image Sharpening Using Frequency Domain Filters
3.9.1 Ideal Highpass Filters
3.9.2 Butterworth Highpass Filters
3.9.3 Gaussian High Pass Filters
3.9.4 The Fourier transform of Laplacian operator
3.9.5 Unsharp filter, Highboost Filtering, and High-Frequency-Emphasis Filtering
3.9.6 Frequency Domain Homomorphic Filtering
References and Further Reading

4. Image Restoration
4.1 Image degradation and restoration
4.2 Noise analysis
4.3 Restoration with spatial analysis
4.4 Restoration with frequency analysis
4.5 Motion blur and image restoration
4.6 Geometric transformation

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