book

Digital Signal and Image Processing Using MATLAB

by Gérard Blanchet, Maurice Charbit

May 2006

Intermediate to advanced

763 pages

16h 57m

English

Wiley

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1 Variables1.1 Vectors and matrices1.2 Arrays1.3 Cells and structures2 Operations and functions2.1 Matrix operations2.2 Pointwise operations2.3 Constants and initialization2.4 Predefined matrices2.5 Mathematical functions2.6 Matrix functions2.7 Other useful functions2.8 Logical operators on boolean variables2.9 Program loops3 Graphically displaying results4 Converting numbers to character strings5 Input/output6 Program writing
1.1 The concept of signal1.1.1 A few signals1.1.2 Spectral representation of signals1.2 The Concept of system1.3 Summary

2.1 The sampling theorem2.1.1 Perfect reconstruction2.1.2 Digital-to-analog conversion2.2 Plotting a signal as a function of time2.3 Spectral representation2.3.1 Discrete-time Fourier transform (DTFT)2.3.2 Discrete Fourier transform (DFT)2.4 Fast Fourier transform
3.1 Spectral accuracy and resolution3.1.1 Observation of a complex exponential3.1.2 Plotting accuracy of the DTFT3.1.3 Frequency resolution3.1.4 Effects of windowing on the resolution3.2 Short term Fourier transform3.3 Summing up3.4 Application examples and exercises3.4.1 Amplitude modulations3.4.2 Frequency modulation
4.1 Definitions and properties4.2 The z-transform4.2.1 Definition and properties4.2.2 A few examples4.3 Transforms and linear filtering4.4 Difference equations and rational TF filters4.4.1 Stability considerations4.4.2 FIR and IIR filters4.4.3 Causal solution and initial conditions4.4.4 Calculating the responses4.4.5 Stability and the Jury test4.5 Connection between gain and poles/zeros4.6 Minimum phase filters4.7 Filter design methods4.7.1 Going from the continuous-time filter to the discretetime filter4.7.2 FIR filter design using the window method4.7.3 IIR filter design4.8 Oversampling and undersampling4.8.1 Oversampling4.8.2 Undersampling
5.1 Filter implementation5.1.1 Examples of filter structures5.1.2 Distributing the calculation load in an FIR filter5.1.3 FIR block filtering5.1.4 FFT filtering5.2 Filter banks5.2.1 Decimation and expansion5.2.2 Filter banks
6.1 Introduction6.1.1 Image display, color palette6.1.2 Importing images6.1.3 Arithmetical and logical operations6.2 Geometric transformations of an image6.2.1 The typical transformations6.2.2 Aligning images6.3 Frequential content of an image6.4 Linear filtering6.5 Other operations on images6.5.1 Undersampling6.5.2 Oversampling6.5.3 Contour detection6.5.4 Median filtering6.5.5 Maximum enhancement6.5.6 Image binarization6.5.7 Morphological filtering of binary images6.6 JPEG lossy compression6.6.1 Basic algorithm6.6.2 Writing the compression function6.6.3 Writing the decompression function6.7 Watermarking6.7.1 Spatial image watermarking6.7.2 Spectral image watermarking
7.1 Random phenomena in signal processing7.2 Basic concepts of random variables7.3 Common probability distributions7.3.1 Uniform probability distribution on (a, b)7.3.2 Real Gaussian random variable7.3.3 Complex Gaussian random variable7.3.4 Generating the common probability distributions7.3.5 Estimating the probability density7.3.6 Gaussian random vectors7.4 Generating an r.v. with any type of p.d.7.5 Uniform quantization
8.1 Introduction8.2 Wide-sense stationary processes8.2.1 Definitions and properties of WSS processes8.2.2 Spectral representation of a WSS process8.2.3 Sampling a WSS process8.3 Estimating the covariance8.4 Filtering formulae for WSS random processes8.5 MA, AR and ARMA time series8.5.1 Q order MA (Moving Average) process8.5.2 P order AR (Autoregressive) Process8.5.3 The Levinson algorithm8.5.4 ARMA (P, Q) process
9.1 Non-parametric estimation of the PSD9.1.1 Estimation from the autocovariance function9.1.2 Estimation based on the periodogram9.2 Parametric estimation9.2.1 AR estimation9.2.2 Estimating the spectrum of an AR process9.2.3 The Durbin method of MA estimation
10.1 Estimating the amplitudes and the frequencies10.1.1 The case of a single complex exponential10.1.2 Real harmonic mixtures10.1.3 Complex harmonic mixtures10.2 Periodograms and the resolution limit10.3 High resolution methods10.3.1 Periodic signals and recursive equations10.3.2 The Prony method10.3.3 The MUSIC algorithm10.3.4 Introduction to array processing
11.1 The projection theorem11.2 The least squares method11.2.1 Formulating the problem11.2.2 The linear model11.2.3 The least squares estimator11.2.4 The RLS algorithm (recursive least squares)11.2.5 Identifying the impulse response of a channel11.3 Linear predictions of the WSS processes11.3.1 Yule-Walker equations11.3.2 Predicting a WSS harmonic process11.3.3 Predicting a causal AR-P process11.3.4 Reflection coefficients and lattice filters11.4 Wiener filtering11.4.1 Finite impulse response solution11.4.2 Gradient algorithm11.4.3 Wiener equalization11.5 The LMS (least mean square) algorithm11.5.1 The constant step algorithm11.5.2 The normalized LMS algorithm11.5.3 Echo canceling11.6 Application: the Kalman algorithm11.6.1 The Kalman filter11.6.2 The vector case
12.1 Simulation of continuous-time systems12.1.1 Simulation by approximation12.1.2 Exact model simulation12.2 Dual Tone Multi-Frequency (DTMF)12.3 Speech processing12.3.1 A speech signal model12.3.2 Compressing a speech signal12.4 DTW12.5 Modifying the duration of an audio signal12.5.1 PSOLA12.5.2 Phase vocoder12.6 Quantization noise shaping12.7 Elimination of the background noise in audio12.8 Eliminating the impulse noise12.8.1 The signal model12.8.2 Click detection12.8.3 Restoration12.9 Tracking the cardiac rhythm of the fetus12.9.1 Objectives12.9.2 Separating the EKG signals12.9.3 Estimating cardiac rhythms12.10 Extracting the contour of a coin12.11 Principal component analysis (PCA)12.11.1 Determining the principal components12.11.2 2-Dimension PCA12.11.3 Linear discriminant analysis (LDA)12.12 Separating an instantaneous mixture12.13 Matched filters in radar telemetry12.14 Kalman filtering12.15 Compression12.15.1 Scalar quantization12.15.2 Vector quantization12.16 Digital communications12.16.1 Introduction12.16.2 8-phase shift keying (PSK)12.16.3 PAM modulation12.16.4 Spectrum of a digital signal12.16.5 The Nyquist criterion in digital communications12.16.6 The eye pattern12.16.7 PAM modulation on the Nyquist channel12.17 Linear equalization and the Viterbi algorithm12.17.1 Linear equalization12.17.2 The Viterbi algorithm
H1 Signal fundamentalsH2 Discrete time signals and samplingH3 Spectral observationH4 Linear filtersH5 Filter implementationH6 An Introduction to image processingH7 Random variablesH8 Random processesH9 Continuous spectra estimationH10 Discrete spectra estimationH11 The least squares methodH12 Selected topics
A1 Fourier transformA2 Discrete time Fourier transformA3 Discrete Fourier transformA4 z-TransformA5 Jury criterionA6 FFT filtering algorithms revisited

Overview

This title provides 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.

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ISBN: 9781905209132Purchase book

Digital Signal and Image Processing Using MATLAB

by Gérard Blanchet, Maurice Charbit

Overview

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Overview

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,and much more.

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Digital Signal Processing Using Matlab

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Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.