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Book Categories |
1 | Introduction | 1 |
1.1 | Digital Holography and Evolution of Imaging Techniques | 1 |
1.2 | Contents of this Book | 5 |
2 | Optical Signals and Transforms | 11 |
2.1 | Mathematical Models of Optical Signals | 11 |
2.2 | Signal Transformations | 24 |
2.3 | Imaging Systems and Integral Transforms | 27 |
2.4 | Fourier Transform and its Derivatives | 39 |
2.5 | Imaging from Projections: Radon and Abel Transforms | 57 |
2.6 | Multi Resolution Imaging: Wavelet Transforms | 61 |
2.7 | Sliding Window Transforms and "Time-Frequency" (Space-Transform) Signal Representation | 63 |
2.8 | Stochastic Transformations and Statistical Models | 67 |
3 | Digital Representation of Signals | 79 |
3.1 | Principles of Signal Digitization | 79 |
3.2 | Signal Discretization as Expansion Over a Set of Basis Functions. Typical Basis Functions and Classification | 80 |
3.3 | Shift (Convolution) Bases Functions and Sampling Theorem | 106 |
3.4 | Multi-Resolution Sampling | 129 |
3.5 | Unconventional Digital Imaging Methods | 131 |
3.6 | Principles of Signal Scalar Quantization | 133 |
3.7 | Basics of Signal Coding and Data Compression | 147 |
4 | Digital Representation of Signal Transformations | 161 |
4.1 | The Principles | 161 |
4.2 | Discrete Representation of Convolution Integral. Digital Filters | 170 |
4.3 | Discrete Representation of Fourier Integral Transform | 170 |
4.4 | Discrete Representation of Fresnel Integral Transform | 199 |
5 | Methods and Algorithms of Digital Filtering | 211 |
5.1 | Filtering in Signal Domain | 211 |
5.2 | Filtering in Transform Domain | 226 |
5.3 | Combined Algorithms for Computing DFT and DCT of Real Valued Signals | 231 |
6 | Fast Algorithms | 239 |
6.1 | The Principle of Fast Fourier Transforms | 239 |
6.2 | Matrix Techniques in Fast Transforms | 243 |
6.3 | Transforms and their Fast Algorithms in Matrix Representation | 249 |
6.4 | Pruned Algorithms | 264 |
6.5 | Quantized DFT | 269 |
7 | Statistical Methods and Algorithms | 275 |
7.1 | Measuring Signal Statistical Characteristics | 275 |
7.2 | Digital Statistical Models and Monte Carlo Methods | 291 |
7.3 | Statistical (Monte Carlo) Simulation. Case Study: Speckle Noise Phenomena in Coherent Imaging and Digital Holography | 305 |
8 | Sensor Signal Perfecting, Image Restoration, Reconstruction and Enhancement | 313 |
8.1 | Mathematical Models of Imaging Systems | 313 |
8.2 | Linear Filters for Image Restoration | 315 |
8.3 | Sliding Window Transform Domain Adaptive Signal Restoration | 328 |
8.4 | Multi-Component Image Restoration | 340 |
8.5 | Filtering Impulse Noise | 343 |
8.6 | Methods for Correcting Gray Scale Nonlinear Distortions | 348 |
8.7 | Image Reconstruction | 353 |
8.8 | Image Enhancement | 361 |
9 | Image Resampling and Geometrical Transformations | 373 |
9.1 | Principles of Image Resampling | 373 |
9.2 | Nearest Neighbor, Linear and Spline Interpolation Methods | 376 |
9.3 | Algorithms of Discrete Sinc-Interpolation | 380 |
9.4 | Application examples | 395 |
10 | Signal Parameter Estimation and Measurement. Object Localization | 411 |
10.1 | Problem Formulation. Optimal Statistical Estimates | 411 |
10.2 | Localization of an Object in the Presence of Additive White Gaussian Noise | 414 |
10.3 | Performance of the Optimal Localization Device | 421 |
10.4 | Localization of an Object in the Presence of Additive Correlated Gaussian Noise | 443 |
10.5 | Optimal Localization in Color and Multi Component Images | 449 |
10.6 | Object Localization in the Presence of Multiple Nonoverlappning Non-Target Objects | 454 |
11 | Target Location in Clutter | 461 |
11.1 | Problem Formulation | 461 |
11.2 | Localization of Precisely Known Objects: Spatially Homogeneous Optimality Criterion | 465 |
11.3 | Localization of Inexactly Known Object: Spatially Homogeneous Criterion | 474 |
11.4 | Localization Methods for Spatially Inhomogeneous Criteria | 478 |
11.5 | Object Localization and Image Blur | 483 |
11.6 | Object Localization and Edge Detection. Selection of Reference Objects for Target Tracking | 485 |
11.7 | Optimal Adaptive Correlator and Optical Correlators | 490 |
11.8 | Target Locating in Color and Multi Component Images | 499 |
12 | Nonlinear Filters in Signal/Image Processing | 509 |
12.1 | Classification Principles | 510 |
12.2 | Filter Classification Tables | 519 |
12.3 | Practical Examples | 527 |
13 | Computer Generated Holograms | 541 |
13.1 | Mathematical Models | 541 |
13.2 | Methods for Encoding and Recording Computer Generated Holograms | 548 |
13.3 | Reconstruction of Computer Generated Holograms | 565 |
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Add Digital Holography and Digital Image Processing:: Principles, Methods, Algorithms, Digital holography and digital image processing are twins born by computer era. They share origin, theoretical base, methods and algorithms. The present book describes these common fundamentals principles, methods and algorithms including image and hologr, Digital Holography and Digital Image Processing:: Principles, Methods, Algorithms to the inventory that you are selling on WonderClubX
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Add Digital Holography and Digital Image Processing:: Principles, Methods, Algorithms, Digital holography and digital image processing are twins born by computer era. They share origin, theoretical base, methods and algorithms. The present book describes these common fundamentals principles, methods and algorithms including image and hologr, Digital Holography and Digital Image Processing:: Principles, Methods, Algorithms to your collection on WonderClub |