國立虎尾科技大學 |

Local approximation techniques in signal and image processing

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Local approximation techniques in signal and image processing/ Vladimir Katkovnik, Karen Egiazarian, and Jaakko Astola.
作者:	Katkovnik, V. A.
其他作者:	Astola, Jaakko.
出版者:	Bellingham, Wash. (1000 20th St. Bellingham WA 98225-6705 USA) :SPIE, : 2006.,
面頁冊數:	1 online resource (xvii, 553 p. : ill.) :digital file. :
附註:	"SPIE digital library."
標題:	Approximation theory. -
電子資源:	http://dx.doi.org/10.1117/3.660178
ISBN:	9780819478337 (electronic)

Local approximation techniques in signal and image processing
Katkovnik, V. A.

Local approximation techniques in signal and image processing[electronic resource] /Vladimir Katkovnik, Karen Egiazarian, and Jaakko Astola. - Bellingham, Wash. (1000 20th St. Bellingham WA 98225-6705 USA) :SPIE,2006. - 1 online resource (xvii, 553 p. : ill.) :digital file. - SPIE Press monograph ;PM157. - SPIE Press monograph ;PM103..

"SPIE digital library."

Includes bibliographical references (p. 535-546) and index.

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.

Restricted to subscribers or individual electronic text purchasers.

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.

System requirements: Adobe Acrobat Reader.

ISBN: 9780819478337 (electronic)

Standard No.: 10.1117/3.660178doiSubjects--Topical Terms:

527707
Approximation theory.

LC Class. No.: TK5102.9 / .K38 2006e

Dewey Class. No.: 621.382/2

Local approximation techniques in signal and image processing
LDR:04871nam 2200529 a 4500 001 730979
003 SPIE
005 20090902113538.0
006 m e d
007 cr bn |||m|||a
008 130501s2006 waua fob 001 0 eng
010 $z 2006042318
020 $a 9780819478337 (electronic)
020 $z 0819460923 (print)
020 $z 9780819460929 (print)
024 7 $a 10.1117/3.660178 $2 doi
035 $a (OCoLC)435971842
035 $a (CaBNVSL)gtp00535507
035 $a 9780819478337
040 $a CaBNVSL $c CaBNVSL $d CaBNVSL
050 4 $a TK5102.9 $b .K38 2006e
082 0 0 $a 621.382/2 $2 22
100 1 $a Katkovnik, V. A. $q (Vladimir Akovlevich) $3 877222
245 1 0 $a Local approximation techniques in signal and image processing $h [electronic resource] / $c Vladimir Katkovnik, Karen Egiazarian, and Jaakko Astola.
260 $a Bellingham, Wash. (1000 20th St. Bellingham WA 98225-6705 USA) : $b SPIE, $c 2006.
300 $a 1 online resource (xvii, 553 p. : ill.) : $b digital file.
490 1 $a SPIE Press monograph ; $v PM157
500 $a "SPIE digital library."
504 $a Includes bibliographical references (p. 535-546) and index.
505 0 $a 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.
505 0 $a 2. Discrete LPA -- 2.1. Introduction -- 2.2. Basis of LPA -- 2.3. Kernel LPA estimates -- 2.4. Nonparametric regression -- 2.5. Nonparametric interpolation.
505 0 $a 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.
505 0 $a 4. Integral LPA -- 4.1. Integral kernel estimators -- 4.2. Analytical kernels -- 4.3. Generalized singular functions -- 4.4. Potential derivative estimates.
505 0 $a 5. Discrete LPA accuracy -- 5.1. Bias and variance of estimates -- 5.2. Ideal scale -- 5.3. Accuracy of potential differentiators.
505 0 $a 6. Adaptive-scale selection -- 6.1. ICI rule -- 6.2. Multiple-window estimation -- 6.3. Denoising experiments.
505 0 $a 7. Anisotropic LPA -- 7.1. Directional signal processing -- 7.2. Directional LPA -- 7.3. Numerical directional kernels.
505 0 $a 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.
505 0 $a 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.
505 0 $a 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.
505 0 $a 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.
505 0 $a 12. Photon imaging -- 12.1. Direct Poisson observations -- 12.2. Indirect Poisson observations -- 12.3. Local ML Poisson inverse -- 12.4. Computerized tomography.
505 0 $a 13. Multiresolution analysis -- 13.1. MR analysis: basic concepts -- 13.2. Nonparametric LPA spectrum -- 13.3. Thresholding -- 13.4. Parallels with wavelets.
505 0 $a 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.
506 $a Restricted to subscribers or individual electronic text purchasers.
520 $a 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.
530 $a Also available in print version.
538 $a System requirements: Adobe Acrobat Reader.
538 $a Mode of access: World Wide Web.
650 0 $a Approximation theory. $3 527707
650 0 $a Image processing $x Mathematics. $3 600009
650 0 $a Signal processing $x Mathematics. $3 528164
700 1 $a Astola, Jaakko. $3 771800
700 1 $a Egiazarian, K. $q (Karen), $d 1959- $3 847937
710 2 $a Society of Photo-optical Instrumentation Engineers. $3 700253
830 0 $a SPIE Press monograph ; $v PM103. $3 877204
856 4 0 $u http://dx.doi.org/10.1117/3.660178