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عدد المساهمات : 19002 التقييم : 35506 تاريخ التسجيل : 01/07/2009 الدولة : مصر العمل : مدير منتدى هندسة الإنتاج والتصميم الميكانيكى
| موضوع: كتاب Advanced Digital Imaging Laboratory Using MATLAB - Second Edition الجمعة 12 أبريل 2024, 12:56 am | |
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أخواني في الله أحضرت لكم كتاب 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
و المحتوى كما يلي :
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 #ماتلاب,#متلاب,#Matlab,#مات_لاب,#مت_لاب,
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