Yaroslavsky Theoretical Foundations of Digital Imaging Using MATLAB®

1. Auflage 2012
ISBN: 978-1-4665-9219-3
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 514 Seiten

Reihe: Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series

With the ubiquitous use of digital imaging, a new profession has emerged: imaging engineering. Designed for newcomers to imaging science and engineering, Theoretical Foundations of Digital Imaging Using MATLAB® treats the theory of digital imaging as a specific branch of science. It covers the subject in its entirety, from image formation to image perfecting.

Based on the author’s 50 years of working and teaching in the field, the text first addresses the problem of converting images into digital signals that can be stored, transmitted, and processed on digital computers. It then explains how to adequately represent image transformations on computers. After presenting several examples of computational imaging, including numerical reconstruction of holograms and virtual image formation through computer-generated display holograms, the author introduces methods for image perfect resampling and building continuous image models. He also examines the fundamental problem of the optimal estimation of image parameters, such as how to localize targets in images. The book concludes with a comprehensive discussion of linear and nonlinear filtering methods for image perfecting and enhancement.

Helping you master digital imaging, this book presents a unified theoretical basis for understanding and designing methods of imaging and image processing. To facilitate a deeper understanding of the major results, it offers a number of exercises supported by MATLAB programs, with the code available at www.crcpress.com.

Zielgruppe

Graduate students, researchers, and advanced undergraduate students in physics, engineering, and computer science.

Autoren/Hrsg.

Yaroslavsky, Leonid P.

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung

Inhaltsverzeichnis

Introduction
Imaging Goes Digital

Mathematical Preliminaries
Mathematical Models in Imaging
Signal Transformations
Imaging Systems and Integral Transforms
Statistical Models of Signals and Transformations

Image Digitization
Principles of Signal Digitization
Signal Discretization
Image Sampling
Alternative Methods of Discretization in Imaging Devices
Single Scalar Quantization
Basics of Image Data Compression
Basics of Statistical Coding
Discrete Signal Transformations
Basic Principles of Discrete Representation of Signal Transformations
Discrete Representation of the Convolution Integral
Discrete Representation of Fourier Integral Transform
Discrete Representation of Fresnel Integral Transform
Discrete Representation of Kirchhoff Integral
Hadamard, Walsh, and Wavelet Transforms
Discrete Sliding Window Transforms and “Time-Frequency” Signal Representation
Digital Image Formation and Computational Imaging
Image Recovery from Sparse or Nonuniformly Sampled Data
Digital Image Formation by Means of Numerical Reconstruction of Holograms
Computer-Generated Display Holography
Computational Imaging Using Optics-Less Lambertian Sensors

Image Resampling and Building Continuous Image Models
Perfect Resampling Filter
Fast Algorithms for Discrete Sinc Interpolation and Their Applications
Discrete Sinc Interpolation versus Other Interpolation Methods: Performance Comparison
Numerical Differentiation and Integration
Local (“Elastic”) Image Resampling: Sliding Window Discrete Sinc Interpolation Algorithms
Image Data Resampling for Image Reconstruction from Projections
Image Parameter Estimation: Case Study—Localization of Objects in Images
Localization of Target Objects in the Presence of Additive Gaussian Noise
Target Localization in Cluttered Images

Image Perfecting
Image Perfecting as a Processing Task
Possible Approaches to Restoration of Images Distorted by Blur and Contaminated by Noise
MMSE-Optimal Linear Filters for Image Restoration
Sliding Window Transform Domain Adaptive Image Restoration
Multicomponent Image Restoration and Data Fusion
Filtering Impulse Noise
Correcting Image Grayscale Nonlinear Distortions
Nonlinear Filters for Image Perfecting

Index

Exercises and References appear at the end of each chapter.

Über Autor(innen)

Leonid P. Yaroslavsky is a professor emeritus at Tel Aviv University. A fellow of the Optical Society of America, Dr. Yaroslavsky has authored more than 100 papers on digital image processing and digital holography.