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雷光月

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[资源] 2014书籍超级帖八一〇《Advanced Digital Imaging Laboratory Using MATLAB》

书名Advanced Digital Imaging
Laboratory Using MATLAB
作者Leonid P Yaroslavsky
页数123
出版社IOP Publishing, Bristol, UK
ISBN 978-0-750-31050-5 (ebook)
ISBN 978-0-750-31051-2 (print)
Preface viii
Author biography ix
1 Introduction 1-1
1.1 General remarks about the book 1-1
1.2 Instructions for readers 1-5
2 Image digitization 2-1
2.1 Introduction 2-1
2.2 Image discretization 2-2
2.2.1 Signal discretization as expansion over a set of basis functions 2-3
2.2.2 Image sampling 2-5
2.2.3 Questions for self-testing 2-8
2.3 Signal scalar quantization 2-9
2.3.1 Exercises 2-9
2.3.2 Questions for self-testing 2-14
2.4 Image compression 2-15
2.4.1 Exercises 2-15
2.4.2 Questions for self-testing 2-18
3 Digital image formation and computational imaging 3-1
3.1 Introduction 3-1
3.2 Image recovery from sparse irregularly sampled data. Recovery
of images with occlusions
3-1
3.3 Numerical reconstruction of holograms 3-4
3.4 Image reconstruction from projections 3-7
3.5 Questions for self-testing 3-9
4 Image resampling and building continuous image models 4-1
4.1 Introduction 4-1
4.2 Signal/image subsampling through fractional shifts 4-2
4.3 Image resampling using ‘continuous’ image models 4-4
4.4 The three-step rotation algorithm 4-4
4.5 Comparison of image resampling methods 4-7
v
4.6 Comparison of signal numerical differentiation and
integration methods
4-11
4.7 Questions for self-testing 4-14
5 Image and noise statistical characterization and diagnostics 5-1
5.1 Introduction 5-1
5.2 Image histograms 5-1
5.3 Image local moments and order statistics 5-2
5.4 Pixel attributes and neighborhoods 5-3
5.5 Image autocorrelation functions and power spectra 5-6
5.6 Image noise 5-8
5.7 Empirical diagnostics of image noise 5-13
5.8 Questions for self-testing 5-18
6 Statistical image models and pattern formation 6-1
6.1 Introduction 6-1
6.2 PWN models 6-1
6.3 LF models 6-4
6.3.1 Introduction 6-4
6.3.2 Textures with circular ‘ring of stars’, circular and ring-shaped
spectra. ‘Fractal’ textures with ‘1/f P’-type spectra
6-5
6.3.3 Imitation of natural textures 6-6
6.3.4 Spatially inhomogeneous textures with controlled local spectra 6-6
6.4 PWN&LF and LF&PWN models 6-8
6.5 Evolutionary models 6-10
6.6 Questions for self-testing 6-12
7 Image correlators for detection and localization of objects 7-1
7.1 Introduction 7-1
7.2 Localization of a target on images contaminated with additive
uncorrelated Gaussian noise. Normal and anomalous localization
errors
7-2
7.3 ‘Matched filter’ correlator versus signal-to-clutter ratio optimal
correlator and local versus global signal-to-clutter ratio optimal
correlators
7-5
7.4 Object localization and image edges 7-8
7.5 Questions for self-testing 7-9
Advanced Digital Imaging Laboratory Using MATLAB®
vi
8 Methods of image perfecting 8-1
8.1 Introduction 8-1
8.2 Correcting imaging system transfer functions 8-1
8.3 Filtering periodical interferences. Filtering ‘banding’ noise 8-4
8.4 ‘Ideal’ and empirical Wiener filtering for image denoising and
deblurring
8-8
8.5 Local adaptive filtering for image denoising 8-10
8.6 Filtering impulsive noise using linear filters 8-12
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