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2014书籍超级帖八一〇《Advanced Digital Imaging Laboratory Using MATLAB》
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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  1998fa85ecb5af7b1e6fcedc579f1426-d.jpg |
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