Fundamentals of digital imaging:
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| Main Authors: | , |
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| Format: | Book |
| Language: | English |
| Published: |
Cambridge [u.a.]
Cambridge Univ. Press
2008
|
| Edition: | 1. publ. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | XVII, 532 S. Ill., graph. Darst. |
| ISBN: | 052186853X 9780521868532 |
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| 100 | 1 | |a Trussell, Joel |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Fundamentals of digital imaging |c H. J. Trussell and M. J. Vrhel |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2008 | |
| 300 | |a XVII, 532 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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| 700 | 1 | |a Vrhel, Michael J. |e Verfasser |0 (DE-588)1018266291 |4 aut | |
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| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016600770&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |3 Klappentext |
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| _version_ | 1804137809811865600 |
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| adam_text | Contents
Preface
page
xv
Acknowledgments
xviii
Introduction
і
1.1
Digital imaging: overview
1
.2
Digital imaging: short history
2
.3
Applications
4
.4
Methodology
4
.5
Prerequisite knowledge
5
.6
Overview of the book
6
Mathematical representation
8
2.1
Images as functions
8
2.1.1
Continuous vs. discrete variables
11
2.1.2
Deterministic vs. stochastic
11
2.1.3
Philosophical aspects of the problem
12
2.1.4
Two dimensions vs. one dimension
13
2.2
Systems and operators
14
2.2.1
Amplitude scaling
15
2.2.2
Spatial translation
16
2.2.3
Spatial scaling
16
2.3
Linear systems (operators)
17
2.4
Sampling representation
20
2.5
Shift-invariant (space-invariant) linear systems
22
2.5.1
One-dimensional continuous convolution
22
2.5.2
Two-dimensional continuous convolution
23
2.5.3
Discrete convolution
25
2.6
Differential operators
32
2.7
Matrix representation of images
33
2.8
Problems
41
viii Contents
Elementary display of images
45
3.1
Isometric plots
46
3.2
Contour plots
46
3.3
Grayscale graphs
47
3.3.1
Comparative display
49
3.3.2
Grayscale inclusion
53
3.3.3
Display of processed and nonpictorial images
54
3.3.4
Nonlinear mappings and monitor adjustments
56
3.4
Problems
58
Quantization
61
4.1
Appropriate quantization spaces
61
4.2
Basic quantization
64
4.2.1
Uniform quantization
65
4.2.2
Optimal quantization
65
4.3
Companding quantizer
66
4.3.1
Visual quantization
67
4.3.2
Color quantization
68
4.4
Vector quantization
68
4.4.1
Full search vector quantization
69
4.4.2
LBG algorithm (generalized Lloyd)
70
4.4.3
Color palettization (palette design)
71
4.5
Quantization noise
71
4.6
Problems
75
Frequency domain representation
77
5.1
Continuous Fourier transform
78
5.1.1
One-dimensional transform
78
5.1.2
Two-dimensional transform
80
5.1.3
Examples of two-dimensional sinusoids
81
5.2
Properties of 2-D Fourier transforms
83
5.2.1
Relation between analog Fourier transforms
and Fourier series
85
5.3
Derivation of DFT from Fourier transform
88
5.4
Two-dimensional discrete Fourier transform
92
5.4.1
Common DFT pairs
93
5.5
Discrete Fourier transform convolution using
matrices
94
5.6
Computation of 2-D DFT and the FFT
%
5.7
Interpretation and display of Fourier transfonns
97
5.8
Problems HO
Contents ix
6
Spatial
sampling
114
6.1
Ideal
sampling
115
6.1.1
Ideal
sampling,
1
-D
115
6.1.2
Ideal sampling, 2-D
119
6.2
Sampling on nonrectangular lattices
127
6.3
Sampling using finite apertures
129
6.4
Ideal reconstruction of deterministic images
131
6.5
Sampling and reconstruction of stochastic images
132
6.6
Modeling practical reconstruction of images
134
6.6.1
One-dimensional reconstruction
134
6.6.2
Two-dimensional reconstruction
138
6.7
Problems
143
7
Image characteristics
146
7.1
Deterministic properties
146
7.1.1
Basic parameters
147
7.1.2
Bandwidth
151
7.1.3
Subspace concepts
154
7.2
Stochastic properties
162
7.2.1
Basic statistics: mean, variance, correlations
163
7.2.2
Bandwidth
166
7.2.3
Noise
168
7.2.4
Stochastic subspaces
175
7.3
Models for generation of images
177
7.3.1
One-dimensional models
179
7.4
Two-dimensional stochastic image models
180
7.4.1
Prediction regions
181
7.4.2
Determining prediction coefficients
181
7.4.3
Filtered noise
184
7.5
Problems
186
8
Photometry and cotorimetry
191
8.1
Fundamentals of the eye
192
8.2
Radiometry
and photometry
194
8.3
Mathematics of color matching
201
8.3.1
Background
203
8.3.2
Mathematical definition of color matching
204
8.3.3
Properties of color matching functions
208
8.4
Mathematics of color reproduction
217
8.4.1
Additive color systems
218
8.4.2
Subtract!
ve
color systems
220
Contents
8.5
Color spaces
223
8.5.1
Uniform color spaces
223
8.5.2
Device independent and dependent spaces
227
8.5.3
Pseudo-device independent spaces
228
8.6
Color temperature
229
8.7
Color measurement instruments
230
8.7.1
Spectroradiometer
232
8.7.2
Spectrophotometer
234
8.7.3
Colorimeter
236
8.7.4
Densitometer
238
8.8
Problems
241
9
Color sampling
245
9.1
Sampling for color reproduction
245
9.2
Color aliasing
248
9.3
Sampling for color computation
249
9.3.1
Characteristics of color signals
250
9.3.2
Color operations
257
9.33
Signal processing for accurate computation
260
9.3.4
Significance of the errors
262
9.4
Problems
263
10
Image input devices
266
10.1
Scanners
266
10.
1
.1
Optical issues
270
10.1.2
Spectral response and color characterization
273
10.2
Digital still cameras
273
10.2.1
Pipeline
273
10.2.2
The sensor
277
10.2.3
Color separation
278
10.2.4
Demosaicking
279
10.2.5
White balance
281
10.2.6
Appearance
284
10.3
Multispectral and hyperspectral imaging
285
10.4
Problems
286
11
Image output devices and methods
289
11.1
Cathode ray tube monitors
289
11.2
Flat panel displays
292
11.3
Photographic film and paper
300
11.3.1
Monochromatic film
300
Contents xi
і
1.3.2 Colorfilm 302
11.3.3 Photographic
prints
302
11.4
Commercial
printing
304
11.5
Halftone reproduction
306
11.6
Ink-jet devices
332
11.7
Electrophotographic imaging
336
11.8
Dye sublimation
340
11.9
Problems
341
12
Characterization of devices
344
12.1
Goal of characterization
344
12.2
Monochrome
345
12.2.1
Input devices
347
12.2.2
Output devices
349
12.3
Color
353
12.3.1
Device gamut
357
12.3.2
Selection of interchange color space
358
12.33
Calibration and profiling
361
12.3.4
Color management systems
365
12.3.5
Models
366
12.3.6
Profiling of capture devices
367
12.3.7
Profiling of display devices
370
12.3.8
Undercolor removal
374
12.3.9
Perceptual issues
376
12.3.10
Gamut mapping
380
12.4
Problems
386
13
Estimation of image model parameters
390
13.1
Image formation models
390
13.2
Estimation of sensor response
391
13.3
Estimation of noise statistics
393
13.3.1
Estimation of noise variance from experiments
394
13.3.2
Estimation of noise variance from recoded data
395
13.4
Estimation of the point spread function
397
13.4.1
Estimation of the point spread function from a
line spread function
398
13.4.2
Estimation of the line spread function from an
edge spread function
399
13.4.3
Estimation of the point spread function by
spectral analysis
399
xii Contents
13.5
Modeling point
spread
functions
401
13.5
Л
Optical apertures
403
13.5.2
Motion point spread functions
405
13.5.3
Distortions by imaging medium
406
13.5.4
Numerical approximation of point spread functions
408
13.6
Problems
408
14
Image restoration
412
14.1
Restoration of spatial blurs
413
14.1.1
Inverse filter
414
14.1.2
Minimum mean square error (MMSE) filter
416
14.1.3
Spatially varying FIR filters
419
14.1.4
Parametric Wiener filters
419
14.1.5
Power spectral estimation
419
14.1.6
Maximum a posteriori (MAP) restoration
420
14.1.7
Constrained least squares (CLS) restoration
421
14.1.8
Projection onto convex sets
422
14.1.9
Examples of restoration of blurred images
425
14.1.10
Estimation of scanner response
434
14.1.11
Color issues
435
14.2
Color and spectral correction
435
14.2.1
Spectral radiance estimation
436
14.2.2
Constrained estimation
436
14.2.3
Minimum mean square error estimation
437
14.3
Tristimulus value (color) correction
438
14.4
Illuminant
color correction
439
14.4.1
White point mapping
441
14.5
Color photographic film exposure
441
14.6
Problems
442
A Generalized functions and sampling representation
445
A.I Basic definition
445
A.2 Sampling, 1-D
447
A.3 Frequency effects of sampling, 1-D
447
A.4 Sampling, 2-D
448
A.
5
Frequency effects of sampling, 2-D
450
A.6 Generalized sampling
450
В
Digital image manipulation and matrix representation
454
B.I Basic matrix definitions and properties
454
B.2
Kronecker
product
454
B.2.1 Properties of the
Kronecker
product
455
Contents xiii
В.
3
Properties of the trace of matrices
456
B.4 Matrix derivatives
456
B.5 Generalized inverse
(pseudoinverse)
457
B.5.1 Computing A*
459
B.5.
2
Relation to signal recovery
460
B.6 Ill-conditioned matrices (systems)
461
B.7 Properties of DFT matrices
462
С
Stochastic images
464
C.I Basic probability definitions
464
C.
1.1
Common discrete probability distributions
and densities
465
C.I.
2
Common continuous probability densities
465
C.1.3 Central limit theorem
465
С
1.4
Moments: mean, variance
466
C.2 Histograms
466
C.3 Basic joint probability definitions
467
C-3.1 Marginal distributions and densities
468
C.3.
2
Correlation and covariance
468
C.3.3 Independence
469
C.3.
4
Conditional probability distributions and densities;
Bayes
theorem
469
C.4 Stochastic processes
469
C.4.
1
Stationary processes
471
C.5 Transformations of stochastic signals
473
C.6 Effects of shift-invariant linear systems on stochastic signals
474
C.7 Stochastic image models
474
C.7.
1
Estimation of stochastic parameters
475
C.7.
2
Power spectrum computation
476
C.7.3 One-dimensional models
476
C.7.4 Identification of
AR
models
478
C7.5 Maximum entropy extension of
Гуу
(m)
478
C.7.6 Problems with
AR,
MA and
ARMA
model identification
479
C.8 Two-dimensional stochastic image models
480
C8.
1
Causal prediction
480
C.8.
2
Semicausal prediction
480
C.8.3 Noncausal prediction
481
C.9 Determining prediction coefficients
481
C.9.
1
Minimum variance prediction
481
C9.2 Stability
484
C.
10
Spectral factorization of 2-D models
484
С Л0.
1
Solving for finite a(m, n)
484
C.IO^ High resolution spectral estimation
485
xiv Contents
D
Multidimensional
look-up tables
486
D.
1
Introduction
486
D.
2
Mathematics of MLUTs
486
D.2.1 Sample points
487
D.3 Interpolation
488
D.3.1 Finding the cube index
488
D.3.2 Finding
subindices
and weights
489
D.3.3 Interpolation methods
490
D.4 Creation of input device MLUTs
491
D.5 Creation of output device MLUTs
494
E Psychovisual
properties
499
E.
1
Optical system
499
E.2 Sensing elements
501
E.3 Processing elements
502
E.4 Mathematical modeling
504
E.4.1 Weber s law
505
E.4.
2
Spatial-color properties and opponent color spaces
506
E.4.3 sCDELAB
509
References
512
Index
527
Fundamentals of Digital Imaging
Designing complex and practical imaging systems requires a strong foundation in the
accurate capture, display and analysis of digital images. This introduction to digital
imaging covers the core techniques of image capture and the display of monochrome
and color images. The basic tools required to describe sampling and image display on
real devices are presented within a powerful mathematical framework. Starting with an
overview of digital imaging, mathematical representation, and the elementary display
of images, the topics progressively move to quantization, spatial sampling, photometry
and colorimetry, and color sampling, and conclude with the estimation of image model
parameters and image restoration. The characterization of input and output devices is
also covered in detail. The reader will learn the processes used to generate accurate
images, and appreciate the mathematical basis required to test and evaluate new devices.
With numerous illustrations, real-world examples, and end-of-chapter homework
problems, this text is suitable for advanced undergraduate and graduate students taking
courses in digital imaging in electrical engineering and computer science departments.
This will also be an invaluable resource for practitioners in the industry.
Further resources for this title, including instructor-only solutions,
Matlab
scripts
and reference data for problems, are available online at www.cambridge.org/
9780521868532.
H. J. Trussed is Professor and Director of Graduate Programs in the Electrical and
Computer Engineering Department at North Carolina State University. He is an IEEE
Fellow and has written over
200
technical papers.
M. J. Vltiel is the color scientist at Artifex Software, Inc. in Sammamish WA. A senior
member of the IEEE, he is the author of numerous papers and patents in the areas of
image and signal processing.
|
| adam_txt |
Contents
Preface
page
xv
Acknowledgments
xviii
Introduction
і
1.1
Digital imaging: overview
1
.2
Digital imaging: short history
2
.3
Applications
4
.4
Methodology
4
.5
Prerequisite knowledge
5
.6
Overview of the book
6
Mathematical representation
8
2.1
Images as functions
8
2.1.1
Continuous vs. discrete variables
11
2.1.2
Deterministic vs. stochastic
11
2.1.3
Philosophical aspects of the problem
12
2.1.4
Two dimensions vs. one dimension
13
2.2
Systems and operators
14
2.2.1
Amplitude scaling
15
2.2.2
Spatial translation
16
2.2.3
Spatial scaling
16
2.3
Linear systems (operators)
17
2.4
Sampling representation
20
2.5
Shift-invariant (space-invariant) linear systems
22
2.5.1
One-dimensional continuous convolution
22
2.5.2
Two-dimensional continuous convolution
23
2.5.3
Discrete convolution
25
2.6
Differential operators
32
2.7
Matrix representation of images
33
2.8
Problems
41
viii Contents
Elementary display of images
45
3.1
Isometric plots
46
3.2
Contour plots
46
3.3
Grayscale graphs
47
3.3.1
Comparative display
49
3.3.2
Grayscale inclusion
53
3.3.3
Display of processed and nonpictorial images
54
3.3.4
Nonlinear mappings and monitor adjustments
56
3.4
Problems
58
Quantization
61
4.1
Appropriate quantization spaces
61
4.2
Basic quantization
64
4.2.1
Uniform quantization
65
4.2.2
Optimal quantization
65
4.3
Companding quantizer
66
4.3.1
Visual quantization
67
4.3.2
Color quantization
68
4.4
Vector quantization
68
4.4.1
Full search vector quantization
69
4.4.2
LBG algorithm (generalized Lloyd)
70
4.4.3
Color palettization (palette design)
71
4.5
Quantization noise
71
4.6
Problems
75
Frequency domain representation
77
5.1
Continuous Fourier transform
78
5.1.1
One-dimensional transform
78
5.1.2
Two-dimensional transform
80
5.1.3
Examples of two-dimensional sinusoids
81
5.2
Properties of 2-D Fourier transforms
83
5.2.1
Relation between analog Fourier transforms
and Fourier series
85
5.3
Derivation of DFT from Fourier transform
88
5.4
Two-dimensional discrete Fourier transform
92
5.4.1
Common DFT pairs
93
5.5
Discrete Fourier transform convolution using
matrices
94
5.6
Computation of 2-D DFT and the FFT
%
5.7
Interpretation and display of Fourier transfonns
97
5.8
Problems HO
Contents ix
6
Spatial
sampling
114
6.1
Ideal
sampling
115
6.1.1
Ideal
sampling,
1
-D
115
6.1.2
Ideal sampling, 2-D
119
6.2
Sampling on nonrectangular lattices
127
6.3
Sampling using finite apertures
129
6.4
Ideal reconstruction of deterministic images
131
6.5
Sampling and reconstruction of stochastic images
132
6.6
Modeling practical reconstruction of images
134
6.6.1
One-dimensional reconstruction
134
6.6.2
Two-dimensional reconstruction
138
6.7
Problems
143
7
Image characteristics
146
7.1
Deterministic properties
146
7.1.1
Basic parameters
147
7.1.2
Bandwidth
151
7.1.3
Subspace concepts
154
7.2
Stochastic properties
162
7.2.1
Basic statistics: mean, variance, correlations
163
7.2.2
Bandwidth
166
7.2.3
Noise
168
7.2.4
Stochastic subspaces
175
7.3
Models for generation of images
177
7.3.1
One-dimensional models
179
7.4
Two-dimensional stochastic image models
180
7.4.1
Prediction regions
181
7.4.2
Determining prediction coefficients
181
7.4.3
Filtered noise
184
7.5
Problems
186
8
Photometry and cotorimetry
191
8.1
Fundamentals of the eye
192
8.2
Radiometry
and photometry
194
8.3
Mathematics of color matching
201
8.3.1
Background
203
8.3.2
Mathematical definition of color matching
204
8.3.3
Properties of color matching functions
208
8.4
Mathematics of color reproduction
217
8.4.1
Additive color systems
218
8.4.2
Subtract!
ve
color systems
220
Contents
8.5
Color spaces
223
8.5.1
Uniform color spaces
223
8.5.2
Device independent and dependent spaces
227
8.5.3
Pseudo-device independent spaces
228
8.6
Color temperature
229
8.7
Color measurement instruments
230
8.7.1
Spectroradiometer
232
8.7.2
Spectrophotometer
234
8.7.3
Colorimeter
236
8.7.4
Densitometer
238
8.8
Problems
241
9
Color sampling
245
9.1
Sampling for color reproduction
245
9.2
Color aliasing
248
9.3
Sampling for color computation
249
9.3.1
Characteristics of color signals
250
9.3.2
Color operations
257
9.33
Signal processing for accurate computation
260
9.3.4
Significance of the errors
262
9.4
Problems
263
10
Image input devices
266
10.1
Scanners
266
10.
1
.1
Optical issues
270
10.1.2
Spectral response and color characterization
273
10.2
Digital still cameras
273
10.2.1
Pipeline
273
10.2.2
The sensor
277
10.2.3
Color separation
278
10.2.4
Demosaicking
279
10.2.5
White balance
281
10.2.6
Appearance
284
10.3
Multispectral and hyperspectral imaging
285
10.4
Problems
286
11
Image output devices and methods
289
11.1
Cathode ray tube monitors
289
11.2
Flat panel displays
292
11.3
Photographic film and paper
300
11.3.1
Monochromatic film
300
Contents xi
і
1.3.2 Colorfilm 302
11.3.3 Photographic
prints
302
11.4
Commercial
printing
304
11.5
Halftone reproduction
306
11.6
Ink-jet devices
332
11.7
Electrophotographic imaging
336
11.8
Dye sublimation
340
11.9
Problems
341
12
Characterization of devices
344
12.1
Goal of characterization
344
12.2
Monochrome
345
12.2.1
Input devices
347
12.2.2
Output devices
349
12.3
Color
353
12.3.1
Device gamut
357
12.3.2
Selection of interchange color space
358
12.33
Calibration and profiling
361
12.3.4
Color management systems
365
12.3.5
Models
366
12.3.6
Profiling of capture devices
367
12.3.7
Profiling of display devices
370
12.3.8
Undercolor removal
374
12.3.9
Perceptual issues
376
12.3.10
Gamut mapping
380
12.4
Problems
386
13
Estimation of image model parameters
390
13.1
Image formation models
390
13.2
Estimation of sensor response
391
13.3
Estimation of noise statistics
393
13.3.1
Estimation of noise variance from experiments
394
13.3.2
Estimation of noise variance from recoded data
395
13.4
Estimation of the point spread function
397
13.4.1
Estimation of the point spread function from a
line spread function
398
13.4.2
Estimation of the line spread function from an
edge spread function
399
13.4.3
Estimation of the point spread function by
spectral analysis
399
xii Contents
13.5
Modeling point
spread
functions
401
13.5
Л
Optical apertures
403
13.5.2
Motion point spread functions
405
13.5.3
Distortions by imaging medium
406
13.5.4
Numerical approximation of point spread functions
408
13.6
Problems
408
14
Image restoration
412
14.1
Restoration of spatial blurs
413
14.1.1
Inverse filter
414
14.1.2
Minimum mean square error (MMSE) filter
416
14.1.3
Spatially varying FIR filters
419
14.1.4
Parametric Wiener filters
419
14.1.5
Power spectral estimation
419
14.1.6
Maximum a posteriori (MAP) restoration
420
14.1.7
Constrained least squares (CLS) restoration
421
14.1.8
Projection onto convex sets
422
14.1.9
Examples of restoration of blurred images
425
14.1.10
Estimation of scanner response
434
14.1.11
Color issues
435
14.2
Color and spectral correction
435
14.2.1
Spectral radiance estimation
436
14.2.2
Constrained estimation
436
14.2.3
Minimum mean square error estimation
437
14.3
Tristimulus value (color) correction
438
14.4
Illuminant
color correction
439
14.4.1
White point mapping
441
14.5
Color photographic film exposure
441
14.6
Problems
442
A Generalized functions and sampling representation
445
A.I Basic definition
445
A.2 Sampling, 1-D
447
A.3 Frequency effects of sampling, 1-D
447
A.4 Sampling, 2-D
448
A.
5
Frequency effects of sampling, 2-D
450
A.6 Generalized sampling
450
В
Digital image manipulation and matrix representation
454
B.I Basic matrix definitions and properties
454
B.2
Kronecker
product
454
B.2.1 Properties of the
Kronecker
product
455
Contents xiii
В.
3
Properties of the trace of matrices
456
B.4 Matrix derivatives
456
B.5 Generalized inverse
(pseudoinverse)
457
B.5.1 Computing A*
459
B.5.
2
Relation to signal recovery
460
B.6 Ill-conditioned matrices (systems)
461
B.7 Properties of DFT matrices
462
С
Stochastic images
464
C.I Basic probability definitions
464
C.
1.1
Common discrete probability distributions
and densities
465
C.I.
2
Common continuous probability densities
465
C.1.3 Central limit theorem
465
С
1.4
Moments: mean, variance
466
C.2 Histograms
466
C.3 Basic joint probability definitions
467
C-3.1 Marginal distributions and densities
468
C.3.
2
Correlation and covariance
468
C.3.3 Independence
469
C.3.
4
Conditional probability distributions and densities;
Bayes'
theorem
469
C.4 Stochastic processes
469
C.4.
1
Stationary processes
471
C.5 Transformations of stochastic signals
473
C.6 Effects of shift-invariant linear systems on stochastic signals
474
C.7 Stochastic image models
474
C.7.
1
Estimation of stochastic parameters
475
C.7.
2
Power spectrum computation
476
C.7.3 One-dimensional models
476
C.7.4 Identification of
AR
models
478
C7.5 Maximum entropy extension of
Гуу
(m)
478
C.7.6 Problems with
AR,
MA and
ARMA
model identification
479
C.8 Two-dimensional stochastic image models
480
C8.
1
Causal prediction
480
C.8.
2
Semicausal prediction
480
C.8.3 Noncausal prediction
481
C.9 Determining prediction coefficients
481
C.9.
1
Minimum variance prediction
481
C9.2 Stability
484
C.
10
Spectral factorization of 2-D models
484
С Л0.
1
Solving for finite a(m, n)
484
C.IO^ High resolution spectral estimation
485
xiv Contents
D
Multidimensional
look-up tables
486
D.
1
Introduction
486
D.
2
Mathematics of MLUTs
486
D.2.1 Sample points
487
D.3 Interpolation
488
D.3.1 Finding the cube index
488
D.3.2 Finding
subindices
and weights
489
D.3.3 Interpolation methods
490
D.4 Creation of input device MLUTs
491
D.5 Creation of output device MLUTs
494
E Psychovisual
properties
499
E.
1
Optical system
499
E.2 Sensing elements
501
E.3 Processing elements
502
E.4 Mathematical modeling
504
E.4.1 Weber's law
505
E.4.
2
Spatial-color properties and opponent color spaces
506
E.4.3 sCDELAB
509
References
512
Index
527
Fundamentals of Digital Imaging
Designing complex and practical imaging systems requires a strong foundation in the
accurate capture, display and analysis of digital images. This introduction to digital
imaging covers the core techniques of image capture and the display of monochrome
and color images. The basic tools required to describe sampling and image display on
real devices are presented within a powerful mathematical framework. Starting with an
overview of digital imaging, mathematical representation, and the elementary display
of images, the topics progressively move to quantization, spatial sampling, photometry
and colorimetry, and color sampling, and conclude with the estimation of image model
parameters and image restoration. The characterization of input and output devices is
also covered in detail. The reader will learn the processes used to generate accurate
images, and appreciate the mathematical basis required to test and evaluate new devices.
With numerous illustrations, real-world examples, and end-of-chapter homework
problems, this text is suitable for advanced undergraduate and graduate students taking
courses in digital imaging in electrical engineering and computer science departments.
This will also be an invaluable resource for practitioners in the industry.
Further resources for this title, including instructor-only solutions,
Matlab
scripts
and reference data for problems, are available online at www.cambridge.org/
9780521868532.
H. J. Trussed is Professor and Director of Graduate Programs in the Electrical and
Computer Engineering Department at North Carolina State University. He is an IEEE
Fellow and has written over
200
technical papers.
M. J. Vltiel is the color scientist at Artifex Software, Inc. in Sammamish WA. A senior
member of the IEEE, he is the author of numerous papers and patents in the areas of
image and signal processing. |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Trussell, Joel Vrhel, Michael J. |
| author_GND | (DE-588)1018266291 |
| author_facet | Trussell, Joel Vrhel, Michael J. |
| author_role | aut aut |
| author_sort | Trussell, Joel |
| author_variant | j t jt m j v mj mjv |
| building | Verbundindex |
| bvnumber | BV023418287 |
| classification_rvk | AP 96960 ST 330 ZN 6294 |
| ctrlnum | (OCoLC)611355023 (DE-599)GBV545612217 |
| discipline | Allgemeines Informatik Elektrotechnik / Elektronik / Nachrichtentechnik |
| discipline_str_mv | Allgemeines Informatik Elektrotechnik / Elektronik / Nachrichtentechnik |
| edition | 1. publ. |
| format | Book |
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| id | DE-604.BV023418287 |
| illustrated | Illustrated |
| index_date | 2024-07-02T21:30:11Z |
| indexdate | 2024-07-09T21:18:12Z |
| institution | BVB |
| isbn | 052186853X 9780521868532 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016600770 |
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| owner_facet | DE-824 DE-703 |
| physical | XVII, 532 S. Ill., graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Cambridge Univ. Press |
| record_format | marc |
| spelling | Trussell, Joel Verfasser aut Fundamentals of digital imaging H. J. Trussell and M. J. Vrhel 1. publ. Cambridge [u.a.] Cambridge Univ. Press 2008 XVII, 532 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Digitale Signalverarbeitung (DE-588)4113314-6 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 gnd rswk-swf Bildrekonstruktion (DE-588)4145435-2 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 s DE-604 Digitale Signalverarbeitung (DE-588)4113314-6 s Bildrekonstruktion (DE-588)4145435-2 s Vrhel, Michael J. Verfasser (DE-588)1018266291 aut Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016600770&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016600770&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Trussell, Joel Vrhel, Michael J. Fundamentals of digital imaging Digitale Signalverarbeitung (DE-588)4113314-6 gnd Bildverarbeitung (DE-588)4006684-8 gnd Bildrekonstruktion (DE-588)4145435-2 gnd |
| subject_GND | (DE-588)4113314-6 (DE-588)4006684-8 (DE-588)4145435-2 |
| title | Fundamentals of digital imaging |
| title_auth | Fundamentals of digital imaging |
| title_exact_search | Fundamentals of digital imaging |
| title_exact_search_txtP | Fundamentals of digital imaging |
| title_full | Fundamentals of digital imaging H. J. Trussell and M. J. Vrhel |
| title_fullStr | Fundamentals of digital imaging H. J. Trussell and M. J. Vrhel |
| title_full_unstemmed | Fundamentals of digital imaging H. J. Trussell and M. J. Vrhel |
| title_short | Fundamentals of digital imaging |
| title_sort | fundamentals of digital imaging |
| topic | Digitale Signalverarbeitung (DE-588)4113314-6 gnd Bildverarbeitung (DE-588)4006684-8 gnd Bildrekonstruktion (DE-588)4145435-2 gnd |
| topic_facet | Digitale Signalverarbeitung Bildverarbeitung Bildrekonstruktion |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016600770&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016600770&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT trusselljoel fundamentalsofdigitalimaging AT vrhelmichaelj fundamentalsofdigitalimaging |