【2012】 Theoretical foundations of digital imaging using MATLAB

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收藏 2015-08-07

【2012】 Theoretical foundations of digital imaging using MATLAB

Book 图书名称： Theoretical foundations of digital imaging using MATLAB
Author 作者： Leonid P. Yaroslavsky
Publisher 出版社： CRC Press
Page 页数： 499
Publishing Date 出版时间： Nov 26, 2012
Language 语言： English
Size 大小： 12 MB
Format 格式：pdf 文字版
ISBN: 9781439861417, 1439861412, 9781466587687, 1466587687, 978-1-4665-9219-3, 1466592192
Edition:第1版搜索过论坛，没有该文档

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.

== Table of contents ==
Content: 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.

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