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| Introduction | |
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| Digital Holography and Evolution of Imaging Techniques | |
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| Contents of this Book | |
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| Optical Signals and Transforms | |
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| Mathematical Models of Optical Signals | |
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| Signal Transformations | |
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| Imaging Systems and Integral Transforms | |
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| Fourier Transform and its Derivatives | |
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| Imaging from Projections: Radon and Abel Transforms | |
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| Multi Resolution Imaging: Wavelet Transforms | |
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| Sliding Window Transforms and "Time-Frequency" (Space-Transform) Signal Representation | |
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| Stochastic Transformations and Statistical Models | |
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| Digital Representation of Signals | |
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| Principles of Signal Digitization | |
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| Signal Discretization as Expansion Over a Set of Basis Functions. Typical Basis Functions and Classification | |
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| Shift (Convolution) Bases Functions and Sampling Theorem | |
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| Multi-Resolution Sampling | |
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| Unconventional Digital Imaging Methods | |
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| Principles of Signal Scalar Quantization | |
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| Basics of Signal Coding and Data Compression | |
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| Digital Representation of Signal Transformations | |
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| The Principles | |
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| Discrete Representation of Convolution Integral. Digital Filters | |
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| Discrete Representation of Fourier Integral Transform | |
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| Discrete Representation of Fresnel Integral Transform | |
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| Methods and Algorithms of Digital Filtering | |
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| Filtering in Signal Domain | |
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| Filtering in Transform Domain | |
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| Combined Algorithms for Computing DFT and DCT of Real Valued Signals | |
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| Fast Algorithms | |
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| The Principle of Fast Fourier Transforms | |
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| Matrix Techniques in Fast Transforms | |
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| Transforms and their Fast Algorithms in Matrix Representation | |
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| Pruned Algorithms | |
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| Quantized DFT | |
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| Statistical Methods and Algorithms | |
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| Measuring Signal Statistical Characteristics | |
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| Digital Statistical Models and Monte Carlo Methods | |
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| Statistical (Monte Carlo) Simulation. Case Study: Speckle Noise Phenomena in Coherent Imaging and Digital Holography | |
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| Sensor Signal Perfecting, Image Restoration, Reconstruction and Enhancement | |
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| Mathematical Models of Imaging Systems | |
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| Linear Filters for Image Restoration | |
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| Sliding Window Transform Domain Adaptive Signal Restoration | |
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| Multi-Component Image Restoration | |
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| Filtering Impulse Noise | |
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| Methods for Correcting Gray Scale Nonlinear Distortions | |
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| Image Reconstruction | |
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| Image Enhancement | |
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| Image Resampling and Geometrical Transformations | |
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| Principles of Image Resampling | |
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| Nearest Neighbor, Linear and Spline Interpolation Methods | |
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| Algorithms of Discrete Sinc-Interpolation | |
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| Application examples | |
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| Signal Parameter Estimation and Measurement. Object Localization | |
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| Problem Formulation. Optimal Statistical Estimates | |
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| Localization of an Object in the Presence of Additive White Gaussian Noise | |
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| Performance of the Optimal Localization Device | |
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| Localization of an Object in the Presence of Additive Correlated Gaussian Noise | |
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| Optimal Localization in Color and Multi Component Images | |
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| Object Localization in the Presence of Multiple Nonoverlappning Non-Target Objects | |
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| Target Location in Clutter | |
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| Problem Formulation | |
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| Localization of Precisely Known Objects: Spatially Homogeneous Optimality Criterion | |
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| Localization of Inexactly Known Object: Spatially Homogeneous Criterion | |
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| Localization Methods for Spatially Inhomogeneous Criteria | |
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| Object Localization and Image Blur | |
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| Object Localization and Edge Detection. Selection of Reference Objects for Target Tracking | |
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| Optimal Adaptive Correlator and Optical Correlators | |
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| Target Locating in Color and Multi Component Images | |
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| Nonlinear Filters in Signal/Image Processing | |
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| Classification Principles | |
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| Filter Classification Tables | |
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| Practical Examples | |
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| Computer Generated Holograms | |
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| Mathematical Models | |
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| Methods for Encoding and Recording Computer Generated Holograms | |
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| Reconstruction of Computer Generated Holograms | |