Advanced Digital Imaging Laboratory Using MATLAB – Second Edition

Advanced Digital Imaging Laboratory Using MATLAB – Second Edition
Leonid P Yaroslavsky
Professor Emeritus, School of Electrical Engineering,
Tel Aviv University, Tel Aviv, Palestine
IOP Publishing, Bristol, UK
Contents
Preface to the second edition
Preface
Author biography
1 Introduction
1.1 General remarks about the book
1.2 Instructions for readers
2 Image digitization
2.1 Introduction
2.2 Image discretization
2.2.1 Signal discretization as its expansion over a set
of basis functions
2.2.2 Image sampling
Questions for self-testing
2.3 Signal scalar quantization
2.3.1 Introduction
2.3.2 Quantization of achromatic images
2.3.3 Quantization of color images
2.3.4 Quantization of stereoscopic images
Questions for self-testing
2.4 Image compression
2.4.1 IntroductionQuestions for self-testing
3 Digital image formation and computational
imaging
3.1 Introduction
3.2 Image recovery from sparse irregularly sampled data.
Recovery of images with occlusions
3.3 Numerical reconstruction of holograms
3.3.1 Introduction
3.3.2 Reconstruction of a simulated Fresnel hologram
3.3.3 Reconstruction of a real off-axis hologram
3.3.4 Comparison of Fourier and Convolutional
reconstruction algorithms
3.4 Image reconstruction from projections
Questions for self-testing
4 Image resampling and building continuous image
models
4.1 Introduction
4.2 Signal/image sub-sampling through fractional shifts
4.3 Comparison of DFT-based and DCT-based discrete
sinc interpolations
4.4 Image resampling using ‘continuous’ image models
4.4.1 Extracting image arbitrary profiles
4.4.2 Image local zoom
4.4.3 Image re-sampling according to random pixel
X/Y displacement maps
4.4.4 Cartesian-to-polar coordinate conversion4.5 Three step image rotation algorithm
4.6 Comparison of image resampling methods
4.6.1 Point spread functions and frequency responses
of different interpolators
4.6.2 Multiple rotations of a test image
4.6.3 Image multiple zoom-in/zoom-out
4.7 Comparison of signal numerical differentiation and
integration methods
4.7.1 Discrete frequency responses of numerical
differentiators and integrators
4.7.2 Comparison of numerical differentiation
methods
4.7.3 Iterative differentiation/integration
Questions for self-testing
5 Image and noise statistical characterization and
diagnostics
5.1 Introduction
5.2 Image histograms
5.2.1 Histograms of achromatic images
5.2.2 Histograms of color images
5.3 Image local moments and order statistics
5.4 Pixel attributes and neighborhoods
5.4.1 Pixel statistical attributes
5.4.2 Pixel neighborhoods
5.5 Image autocorrelation functions and power spectra
5.5.1 Image autocorrelation functions5.5.2 Image power spectra
5.6 Image noise
5.6.1 Additive noise
5.6.2 Impulsive noise
5.6.3 Speckle noise
5.7 Empirical diagnostics of image noise
5.7.1 Wide band noise
5.7.2 Moiré noise
5.7.3 Banding noise
Questions for self-testing
6 Statistical image models and pattern formation
6.1 Introduction
6.2 PWN models
6.2.1 Binary spatially inhomogeneous texture with
controlled local probabilities of ‘one’
6.2.2 Spatially inhomogeneous texture with controlled
variances (‘multiplicative noise’)
6.2.3 Spatially inhomogeneous texture with controlled
local histograms
6.3 LF models
6.3.1 Introduction
6.3.2 ‘Ring of stars’, circular and ring-shaped spectra,
‘fractal’ textures
6.3.3 Imitation of natural textures
6.3.4 Spatially inhomogeneous textures with
controlled local spectra6.4 PWN&LF and LF&PWN models
6.5 Evolutionary models
6.5.1 Generating patchy patterns
6.5.2 Generating maze-like patterns
Questions for self-testing
7 Image correlators for detection and localization
of objects
7.1 Introduction
7.2 Localization of a target on images contaminated with
additive uncorrelated Gaussian noise. Normal and
anomalous localization errors
7.2.1 Localization of a target on uniform background
7.2.2 Localization of a character in text
7.2.3 Threshold effect in the probability of false target
detection error
7.3 Normal and anomalous localization errors
7.4 Matched filter correlator versus signal-to-clutter ratiooptimal correlator. Local versus global signal-to-clutter
ratio-optimal correlators
7.4.1 Matched filter correlator versus SCR optimal
correlator
7.4.2 Local versus global SCR optimal correlators
7.5 Object localization and image edges
7.5.1 ‘Image whitening’
7.5.2 Exchange of amplitude spectra of two images
Questions for self-testing8 Methods of image perfecting
8.1 Introduction
8.2 Correcting imaging system transfer functions
8.2.1 Correction of imaging system gray scale
transfer function
8.2.2 Correction of imaging system frequency transfer
function
8.3 Filtering periodical interferences. Filtering ‘banding’
noise
8.3.1 Introduction
8.3.2 Filtering periodical interferences
8.4 Filtering ‘banding’ noise
8.5 ‘Ideal’ and empirical Wiener filtering for image
denoising and deblurring
8.5.1 Introduction
8.5.2 Comparing image deblurring/denoising
capabilities of the ideal and empirical Wiener filters
8.5.3 Inspection of potentials of image restoration
capability of the ideal and empirical Wiener filters
8.6 Local adaptive filtering for image denoising:
achromatic images
8.6.1 Introduction
8.6.2 1D denoising filtering
8.6.3 2D denoising filtering: principle
8.6.4 2D denoising filtering: global versus local
8.7 Local adaptive filtering for image denoising: color
images
8.8 Filtering impulsive noise using linear filters8.9 Image denoising using nonlinear (rank) filters
8.9.1 Filtering additive noise
8.9.2 Filtering impulsive noise
Questions for self-testing
9 Methods of image enhancement
9.1 Introduction
9.2 Enhancement of achromatic images
9.2.1 Contrast enhancement
9.2.2 Edge extraction: Max–Min and Size-EV methods
9.3 Enhancement of color images
9.3.1 Introduction
Questions for self-testing

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