Local approximation techniques in signal and image processing

Medientyp: E-Book

Titel: Local approximation techniques in signal and image processing

Beteiligte: Katkovnik, V. A. [VerfasserIn]; Astola, Jaakko [Sonstige Person, Familie und Körperschaft]; Egiazarian, Karen [Sonstige Person, Familie und Körperschaft]

Körperschaft: SPIE ; Society of Photo-optical Instrumentation Engineers

Erschienen: Bellingham, Wash. <1000 20th St. Bellingham WA 98225-6705 USA>: SPIE, 2006

Umfang: 1 online resource (xvii, 553 p. : ill.)

Sprache: Englisch

DOI: 10.1117/3.660178

ISBN: 9780819478337

Identifikator:

Schlagwörter: Approximation theory ; Image processing Mathematics ; Signal processing Mathematics

Reproduktionsnotiz: Also available in print version

Entstehung:

Anmerkungen: "SPIE digital library. - Includes bibliographical references (p. 535-546) and index
Includes bibliographical references (p. 535-546) and index
Restricted to subscribers or individual electronic text purchasers
1. Introduction -- 1.1. Linear local approximation -- 1.2. Anisotropy -- 1.3. Nonlinear local approximation -- 1.4. Multiresolution analysis -- 1.5. Imaging applications -- 1.6. Overview of the book
Mode of access: World Wide Web
System requirements: Adobe Acrobat Reader

Beschreibung: This book deals with a wide class of novel and efficient adaptive signal processing techniques developed to restore signals from noisy and degraded observations. These signals include those acquired from still or video cameras, electron microscopes, radar, x rays, or ultrasound devices, and are used for various purposes, including entertainment, medical, business, industrial, military, civil, security, and scientific applications. In many cases useful information and high quality must be extracted from the imaging. However, often raw signals are not directly suitable for this purpose and must be processed in some way. Such processing is called signal reconstruction. This book is devoted to a recent and original approach to signal reconstruction based on combining two independent ideas: local polynomial approximation and the intersection of confidence interval rule

1. Introduction -- 1.1. Linear local approximation -- 1.2. Anisotropy -- 1.3. Nonlinear local approximation -- 1.4. Multiresolution analysis -- 1.5. Imaging applications -- 1.6. Overview of the book

10. Nonlinear methods -- 10.1. Why nonlinear methods? -- 10.2. Robust M-estimation -- 10.3. LPA-ICI robust M-estimates -- 10.4. Nonlinear transform methods

11. Likelihood and quasi-likelihood -- 11.1. Local maximum likelihood -- 11.2. Binary and counting observations -- 11.3. Local quasi-likelihood -- 11.4. Quasi-likelihood LPA-ICI algorithms

12. Photon imaging -- 12.1. Direct Poisson observations -- 12.2. Indirect Poisson observations -- 12.3. Local ML Poisson inverse -- 12.4. Computerized tomography

13. Multiresolution analysis -- 13.1. MR analysis: basic concepts -- 13.2. Nonparametric LPA spectrum -- 13.3. Thresholding -- 13.4. Parallels with wavelets

14. Appendix -- 14.1. Analytical regular grid kernels -- 14.2. LPA accuracy -- 14.3. ICI rule -- 14.4. Cross validation -- 14.5. Directional LPA accuracy -- 14.6. Random processes -- 14.7. 3D inverse -- 14.8. Nonlinear methods -- References -- Index

2. Discrete LPA -- 2.1. Introduction -- 2.2. Basis of LPA -- 2.3. Kernel LPA estimates -- 2.4. Nonparametric regression -- 2.5. Nonparametric interpolation

3. Shift-invariant LPA kernels -- 3.1. Regular grid kernels -- 3.2. Vanishing moments -- 3.3. Frequency domain -- 3.4. Numerical shift-invariant kernels -- 3.5. Numerical differentiation

4. Integral LPA -- 4.1. Integral kernel estimators -- 4.2. Analytical kernels -- 4.3. Generalized singular functions -- 4.4. Potential derivative estimates

5. Discrete LPA accuracy -- 5.1. Bias and variance of estimates -- 5.2. Ideal scale -- 5.3. Accuracy of potential differentiators

6. Adaptive-scale selection -- 6.1. ICI rule -- 6.2. Multiple-window estimation -- 6.3. Denoising experiments

7. Anisotropic LPA -- 7.1. Directional signal processing -- 7.2. Directional LPA -- 7.3. Numerical directional kernels

8. Anisotropic LPA-ICI algorithms -- 8.1. Accuracy analysis -- 8.2. Adaptive-scale algorithms -- 8.3. Directional image denoising -- 8.4. Directional differentiation -- 8.5. Shading from depth -- 8.6. Optical flow estimation

9. Image reconstruction -- 9.1. Image deblurring -- 9.2. LPA-ICI deblurring algorithms -- 9.3. Motion deblurring -- 9.4. Super-resolution imaging -- 9.5. Inverse halftoning -- 9.6. 3D inverse

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