Theoretical Foundations of Digital Imaging Using MATLAB®: 1st Edition (Hardback) book cover

Theoretical Foundations of Digital Imaging Using MATLAB®

1st Edition

By Leonid P. Yaroslavsky

CRC Press

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Hardback: 9781439861400
pub: 2012-11-26
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Description

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.

Reviews

"This seminal and highly influential monograph focuses on concrete phenomena for understanding and designing methods of imaging and image processing. … The reader will find a careful discussion of computational imaging, standard material about image reconstruction from sparse sampled data, description of statistically optimal estimation of image numerical parameters, and a presentation of various exercises supported by MATLAB programs."

—Christian Brosseau, Optics & Photonics News

"this is an excellent in-depth review of the fundamentals of digital imaging, best read for its general foundational content" —Contemporary Physics (Aug 2016)

Table of Contents

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.

About the Author

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.

About the Series

Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MAT013000
MATHEMATICS / Graphic Methods
TEC015000
TECHNOLOGY & ENGINEERING / Imaging Systems
TEC019000
TECHNOLOGY & ENGINEERING / Lasers & Photonics