Inverse imaging with Poisson data : from cells to galaxies / Mario Bertero, Patrizia Boccacci, Valeria Ruggiero.

By: Bertero, Mario [author.]Contributor(s): Boccacci, Patrizia [author.] | Ruggiero, Valeria [author.] | Institute of Physics (Great Britain) [publisher.]Material type: TextTextSeries: IOP (Series)Release 6 | IOP expanding physicsPublisher: Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2018]Description: 1 online resource (various pagings) : illustrations (some color)Content type: text Media type: electronic Carrier type: online resourceISBN: 9780750314374 ebookSubject(s): Image processing -- Mathematics | Inverse problems (Differential equations) | Poisson distribution | Imaging systems & technology | TECHNOLOGY & ENGINEERING / Imaging SystemsAdditional physical formats: Print version:: No titleDDC classification: 621.36/7/0151535 LOC classification: TA1637 .B475 2018ebOnline resources: e-book Full-text access Also available in print.
Contents:
1. Introduction -- 1.1. Scope of the book and topic selection -- 1.2. Structure of the book
2. Examples of applications -- 2.1. Fluorescence microscopy -- 2.2. Medical imaging (tomography) -- 2.3. Astronomy
3. Mathematical modeling -- 3.1. Imaging system and forward problem -- 3.2. Ill-posedness of the backward (inverse) problem -- 3.3. Detection and data sampling -- 3.4. Detection and data noise -- 3.5. The discrete models -- 3.6. Supplementary ma
4. Statistical approaches in a discrete setting -- 4.1. Maximum likelihood approach and data-fidelity function -- 4.2. Bayesian regularization -- 4.3. Denoising problems -- 4.4. Selection of the regularization parameter -- 4.5. The Bregman itera
5. Simple reconstruction methods -- 5.1. Expectation maximization (EM) or Richardson-Lucy (RL) method -- 5.2. Ordered subset expectation maximization method -- 5.3. One-step late (OSL) method -- 5.4. Split gradient method (SGM) -- 5.5. Supplemen
6. Optimization methods -- 6.1. Some basic tools : proximity operators and conjugate functions -- 6.2. The family of forward-backward (FB) splitting methods -- 6.3. FB methods for smooth problems of image reconstruction -- 6.4. FB methods for no
7. Numerics -- 7.1. Semi-convergent methods -- 7.2. Methods for edge-preserving regularization -- 7.3. Image reconstruction of real data
8. Specific topics in image deblurring -- 8.1. Super-resolution by data inversion -- 8.2. Boundary artifacts correction -- 8.3. Blind deconvolution -- 8.4. Images with point and smooth sources -- 8.5. Images with space-variant blur
9. Towards a regularization theory -- 9.1. Deterministic regularization approaches -- 9.2. Statistical approaches -- 9.3. Comments and concluding remarks.
Abstract: Inverse Imaging with Poisson Data is an invaluable resource for graduate students, postdocs and researchers interested in the application of inverse problems to the domains of applied sciences, such as microscopy, medical imaging and astronomy.
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IOP Science eBook - EBA TA1637 .B475 2018eb (Browse shelf (Opens below)) Available IOP_20210094

"Version: 20181201"--Title page verso.

Includes bibliographical references.

1. Introduction -- 1.1. Scope of the book and topic selection -- 1.2. Structure of the book

2. Examples of applications -- 2.1. Fluorescence microscopy -- 2.2. Medical imaging (tomography) -- 2.3. Astronomy

3. Mathematical modeling -- 3.1. Imaging system and forward problem -- 3.2. Ill-posedness of the backward (inverse) problem -- 3.3. Detection and data sampling -- 3.4. Detection and data noise -- 3.5. The discrete models -- 3.6. Supplementary ma

4. Statistical approaches in a discrete setting -- 4.1. Maximum likelihood approach and data-fidelity function -- 4.2. Bayesian regularization -- 4.3. Denoising problems -- 4.4. Selection of the regularization parameter -- 4.5. The Bregman itera

5. Simple reconstruction methods -- 5.1. Expectation maximization (EM) or Richardson-Lucy (RL) method -- 5.2. Ordered subset expectation maximization method -- 5.3. One-step late (OSL) method -- 5.4. Split gradient method (SGM) -- 5.5. Supplemen

6. Optimization methods -- 6.1. Some basic tools : proximity operators and conjugate functions -- 6.2. The family of forward-backward (FB) splitting methods -- 6.3. FB methods for smooth problems of image reconstruction -- 6.4. FB methods for no

7. Numerics -- 7.1. Semi-convergent methods -- 7.2. Methods for edge-preserving regularization -- 7.3. Image reconstruction of real data

8. Specific topics in image deblurring -- 8.1. Super-resolution by data inversion -- 8.2. Boundary artifacts correction -- 8.3. Blind deconvolution -- 8.4. Images with point and smooth sources -- 8.5. Images with space-variant blur

9. Towards a regularization theory -- 9.1. Deterministic regularization approaches -- 9.2. Statistical approaches -- 9.3. Comments and concluding remarks.

Inverse Imaging with Poisson Data is an invaluable resource for graduate students, postdocs and researchers interested in the application of inverse problems to the domains of applied sciences, such as microscopy, medical imaging and astronomy.

Also available in print.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

Mario Bertero received an advanced degree in physics from the University of Genova in Italy in 1960, and he obtained the libera docenza in theoretical physics in 1968. He has professorships in mathematics and computer science and was the editor

Title from PDF title page (viewed on January 16, 2019).