Digital signal and image processing using MATLAB. Volume 1, Fundamentals / Gérard Maurice, Blanchet Charbit.

Format:

Book

Author/Creator:

Maurice, Gérard, author.

Charbit, Blanchet, author.

Series:

Digital signal and image processing series.

Digital Signal and Image Processing Series

Language:

English

Subjects (All):

Signal processing--Digital techniques.

Signal processing.

Adaptive signal processing.

Physical Description:

1 online resource (501 p.)

Edition:

Revised and updated second edition.

Place of Publication:

London, England ; Hoboken, New Jersey : iSTE : Wiley, 2014.

Language Note:

English

Summary:

This fully revised and updated second edition presents the most important theoretical aspects of Image and Signal Processing (ISP) for both deterministic and random signals. The theory is supported by exercises and computer simulations relating to real applications. More than 200 programs and functions are provided in the MATLAB language, with useful comments and guidance, to enable numerical experiments to be carried out, thus allowing readers to develop a deeper understanding of both the theoretical and practical aspects of this subject. This fully revised new edition updates: - the introduction to MATLAB programs and functions as well as the Graphically displaying results for 2D displays - Calibration fundamentals for Discrete Time Signals and Sampling in Deterministic signals - image processing by modifying the contrast - also added are examples and exercises.

Contents:

Cover Page

Title Page

Copyright Page

Contents

Foreword

Basic concepts of DSIP

The sampling theorem

Numerical Sequences and DTFT

Filtering and Elements of Filter Design

An introduction to images

Random Processes

Spectral Estimation

The least squares

As a Conclusion

Notations and Abbreviations

Introduction to MATLAB

1 Variables and constants

1.1 Vectors and matrices

1.2 Predefined matrices

1.3 Constants and initialization

1.4 Multidimensional arrays

1.5 Cells and structures

2 Operations and functions

2.1 Matrix operations

2.2 Pointwise operations

2.3 Mathematical functions

2.4 Matrix functions

2.5 Searching elements using min, max, find, etc. functions

2.6 Other useful functions

3 Programming structures

3.1 Logical operators on Boolean variables

3.2 Program loops

3.3 Functions

4 Graphically displaying results

4.1 2D display

4.2 3D display

4.3 Notes on plotting a curve

4.4 Animations

5 Converting numbers to character strings

6 Input/output

7 Program writing

7.1 Developing and testing performances

7.2 Various functions

7.3 Using other languages

Part I: Deterministic Signals

Chapter 1: Signal Fundamentals

1.1 The concept of signal

1.1.1 A few signals

1.1.2 Spectral representation of signals

1. 2 The concept of system

1.3 Summary

Chapter 2: Discrete Time Signals and Sampling

2.1 Fundamentals of sampling

2.1.1 The Poisson formula

2.1.2 Perfect reconstruction

2.1.3 Digital-to-analog conversion

2.2 Plotting a signal as a function of time

2.3 Spectral representation

2.3.1 Discrete-time Fourier transform (DTFT)

2.3.2 Discrete Fourier transform (DFT)

2.3.3 Fast Fourier transform

Chapter 3: Spectral Observation

3.1 Spectral accuracy and resolution.

3.1.1 Observation of a complex exponential

3.1.2 Plotting accuracy of the DTFT

3.1.3 Frequency resolution

3.1.4 Effects of windowing on the resolution

3.2 Short term Fourier transform

3.3 Summing up

3.4 Application examples and exercises

3.4.1 Amplitude modulations

3.4.2 Frequency modulation

Chapter 4: Linear Filters

4.1 Definitions and properties

4.2 The z-transform

4.2.1 Definition and properties

4.2.2 A few examples

4.3 Transforms and linear filtering

4.4 Difference equations and rational TF filters

4.4.1 Stability considerations

4.4.2 FIR and IIR filters

4.4.3 Causal solution and initial conditions

4.4.4 Calculating the responses

4.4.5 Stability and the Jury test

4.5 Connection between gain and poles/zeros

4.6 Minimum phase filters

4.6.1 All-pass filters

4.6.2 Minimum phase filters

4.7 Filter design methods

4.7.1 Going from the continuous-time filter to the discrete-time filter

4.7.2 FIR filter design using the window method

4.7.3 IIR filter design

4.8 Oversampling and undersampling

4.8.1 Oversampling

4.8.2 Undersampling

Chapter 5: An Introduction to Image Processing

5.1 Introduction

5.1.1 Image display, color palette

5.1.2 Importing images

5.1.3 Arithmetical and logical operations

5.2 Color spaces

5.2.1 RGB coding

5.2.2 HSV coding

5.2.3 CMYK coding

5.2.4 How to extract the RGB information from an image

5.2.5 Converting from color to grayscale

5.3 Geometric transformations of an image

5.3.1 The typical transformations

5.3.2 Image registration

5.4 Frequential content of an image

5.5 Linear filtering

5.6 Other operations on images

5.6.1 Undersampling

5.6.2 Oversampling

5.6.3 Contour detection

5.6.4 Median filtering

5.6.5 Image binarization

5.6.6 Modifying the contrast of an image.

5.6.7 Morphological filtering of binary images

5.7 JPEG lossy compression

5.7.1 Basic algorithm

5.7.2 Writing the compression function

5.7.3 Writing the decompression function

Part II: Random Signals

Chapter 6: Random Variables

6.1 Random phenomena in signal processing

6.2 Basic concepts of random variables

6.3 Common probability distributions

6.3.1 Uniform probability distribution on (a, b)

6.3.2 Real Gaussian random variable

6.3.3 Complex Gaussian random variable

6.3.4 Generating the common probability distributions

6.3.5 Estimating the probability density

6.3.6 Gaussian random vectors

6.4 Generating an r. v. with any type of p.d.

6.5 Uniform quantization

Chapter 7: Random Processes

7.1 Introduction

7.2 Wide-sense stationary processes

7.2.1 Definitions and properties of WSS processes

7.2.2 Spectral representation of a WSS process

7.2.3 Sampling a WSS process

7.3 Estimating the covariance

7 .4 Filtering formulae for WSS random processes

7.5 MA, AR and ARMA time series

7.5.1 Q order MA (Moving Average) process

7.5.2 P order AR (AutoRegressive) Process

7.5.3 ARMA (P, Q) process

Chapter 8: Spectra Estimation

8.1 Non-parametric estimation of the psd

8.1.1 Estimation from the autocovariance function

8.1.2 Estimation based on the periodogram

8.2 AR estimation

8.2.1 AR parameters

8.2.2 Estimating the spectrum of an AR process

8.3 Estimating the amplitudes and the frequencies

8.3.1 The case of a single complex exponential

8.3.2 Real harmonic mixtures

8.3.3 Complex harmonic mixtures

8.4 Periodograms and the resolution limit

Chapter 9: The Least Squares Method

9.1 The projection theorem

9.2 The least squares method

9.2.1 Formulating the problem

9.2.2 The linear model

9.2.3 The least squares estimator.

9.2.4 Identifying the impulse response of a channel

9.3 Linear predictions of the WSS processes

9.3.1 Yule-Walker equations

9.3.2 Predicting a WSS harmonic process

9.3.3 Predicting a causal AR-P process

9.4 Wiener filtering

9.4.1 Finite impulse response solution

9.4.2 Gradient algorithm

9.4.3 Wiener equalization

9.5 The LMS (least mean square) algorithm

9.5.1 The constant step algorithm

9.5.2 The normalized LMS algorithm

9.5.3 Echo canceling

Part III: Appendices

Chapter 10: Hints and Solutions

Hl Signal fundamentals

H2 Discrete time signals and sampling

H2.1 {An illustration of the sampling theorem) (see p. 78)

H2.2 (Time domain hermitian symmetry) (see p. 86)

H2.3 (Comparing computation speeds) (see p. 88)

H2.4 (Spectrum of the triangle function) (see p. 88)

H2.5 (Circular convolution of the rectangular signal) (see p. 89)

H2.6 (Delay) (see p. 89)

H2. 7 (FFTs of real sequences) (see p. 93)

H2.8 (Using the FFT) (see p. 93)

H3 Spectral observation

H3.1 (Study of the resolution) (see p. 101)

H3.2 (Effect of the Hamming windowing) (see p. 102)

H3.3 (Short term Fourier transform) (see p. 107)

H3.4 (Visualizing the aliasing with the STFT) (see p. 108)

H3.5 (Effects of sampling and windowing) (see p. 109)

H3.6 (Amplitude modulation) (see p. 110)

H3. 7 (Carrierless double side-band) (see p. 110)

H3.8 (Stereophonic signal) (see p. 111)

H4 Linear filters

H4.1 (Rectangular impulse response filter) (see page 133)

H4.2 (Purely recursive first order) (see page 135)

H4.3 (Purely recursive second order) (see page 138)

H4.4 (Suppressing a sinusoidal component) (see page 141)

H4.5 (All-pass filter, properties of the maximum) (see page 145)

H4.6 (All-pass filter) (see page 145)

H4.7 (Minimum phase filter) (see page 147).

H4.8 (Window method: low-pass filter) (see page 162)

H4.9 (Window method: band-pass filter) (see page 163)

H4.10 (Window method: derivative filter) (see page 163)

H4.11 (Butterworth filter) (see page 166)

H4.12 (Temporal aliasing and DFT) (see page 167)

H4.13 (Interpolation) (see page 169)

H4.14 (Undersampling) (see page 173)

H4.15 (Paralleled undersampling and oversampling) (see page 173)

H5 An introduction to image processing

H5.1 (Logical functions) (see p. 183)

H5.2 (Plane transformation) (see p. 195)

H5.3 (Transformation of a rectangular selection) (see p. 195)

H5.4 (Rectangular filter) (see p. 206)

H5.5 (Conical filter) (see p. 207)

H5.6 (Gaussian smoothing filter) (see p. 207)

H5.7 (Sobel dedvatlve filter) (see p. 209)

H5.8 (Gaussian derivative-smoothing filter) (see p. 213)

H5.9 (Contours using Sobel flliering) (see p. 218)

H5.10 (Median filtering) (see p. 222)

H5.11 (Processing the result of a rotation) (see p. 222)

H5.12 (Application of the oten method) (see p. 226)

H5.13 (Local contrast modifications) (see p. 231)

H5.14 (Writing basic functions) (see p. 236)

H5.15 (Writing the compressed frame) (see p. 237)

H5.16 (Decompression) (see p. 238)

H6 Random variables

H6.1 (Confidence ellipse) (see p. 261)

H6.2 (Poisson distribution) (see p. 264)

H6.3 (Rayleigh distribution) (see p. 266)

H6.4 (Bernoulli distribution) (see p. 266)

H6.5 (Signal-to-quantization noise ratio) (see p. 269)

H7 Random processes

H7.1 (Suppressing an affine trend) (see p. 290)

H7.2 (Smoothing filtering of noise) (see p. 298)

H7.3 (Generating a band limited process) (see p. 300)

H7.4 (Pre-emphasis and de-emphasis) (see p. 300)

H7.5 (Estimation of an FIR filter's impulse response) (see p. 301)

H8 Spectra estimation.

H8.1 (Spectrum estimation using the Welch method) (see p. 323).

Notes:

Previous ed.: 2006.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

1-118-99955-X

1-118-99956-8

OCLC:

886894825