Astronomical image and data analysis /

Astronomical image and data analysis /

Saved in:
Bibliographic Details
Author / Creator:Starck, J.-L. (Jean-Luc), 1965-
Imprint:Berlin ; New York : Springer, c2002.
Description:xi, 289 p. : ill. (some col.) ; 24 cm.
Language:English
Series:Astronomy and astrophysics library, 0941-7834
Subject:
Imaging systems in astronomy -- Data processing.
Imaging systems in astronomy -- Data processing.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/4810712
Hidden Bibliographic Details
Other authors / contributors:Murtagh, Fionn.
ISBN:3540428852 (hc : acid-free)
Notes:Includes bibliographical references (p. [265]-283) and index.
Table of Contents:
  • 1. Introduction to Applications and Methods
  • 1.1. Introduction
  • 1.2. Transformation and Data Representation
  • 1.2.1. Fourier Analysis
  • 1.2.2. Time-Frequency Representation
  • 1.2.3. Time-Scale Representation: The Wavelet Transform
  • 1.2.4. The Radon Transform
  • 1.3. Mathematical Morphology
  • 1.4. Edge Detection
  • 1.4.1. First Order Derivative Edge Detection
  • 1.4.2. Second Order Derivative Edge Detection
  • 1.5. Segmentation
  • 1.6. Pattern Recognition
  • 1.7. Summary
  • 2. Filtering
  • 2.1. Introduction
  • 2.2. Multiscale Transforms
  • 2.2.1. The à Trous Isotropic Wavelet Transform
  • 2.2.2. Multiscale Transforms Compared to Other Data Transforms
  • 2.2.3. Choice of Multiscale Transform
  • 2.2.4. The Multiresolution Support
  • 2.3. Significant Wavelet Coefficients
  • 2.3.1. Definition
  • 2.3.2. Noise Modeling
  • 2.3.3. Automatic Estimation of Gaussian Noise
  • 2.4. Filtering and Wavelet Coefficient Thresholding
  • 2.4.1. Thresholding
  • 2.4.2. Iterative Filtering
  • 2.4.3. Experiments
  • 2.4.4. Iterative Filtering with a Smoothness Constraint
  • 2.5. Haar Wavelet Transform and Poisson Noise
  • 2.5.1. Haar Wavelet Transform
  • 2.5.2. Poisson Noise and Haar Wavelet Coefficients
  • 2.5.3. Experiments
  • 2.6. Summary
  • 3. Deconvolution
  • 3.1. Introduction
  • 3.2. The Deconvolution Problem
  • 3.3. Linear Regularized Methods
  • 3.3.1. Least Squares Solution
  • 3.3.2. Tikhonov Regularization
  • 3.3.3. Generalization
  • 3.4. CLEAN
  • 3.5. Bayesian Methodology
  • 3.5.1. Definition
  • 3.5.2. Maximum Likelihood with GaussianNoise
  • 3.5.3. Gaussian Bayes Model
  • 3.5.4. Maximum Likelihood with Poisson Noise
  • 3.5.5. Poisson Bayes Model
  • 3.5.6. Maximum Entropy Method
  • 3.5.7. Other Regularization Models
  • 3.6. Iterative Regularized Methods
  • 3.6.1. Constraints
  • 3.6.2. Jansson-Van Cittert Method
  • 3.6.3. Other Iterative Methods
  • 3.7. Wavelet-Based Deconvolution
  • 3.7.1. Introduction
  • 3.7.2. Wavelet-Vaguelette Decomposition
  • 3.7.3. Regularization from the Multiresolution Support
  • 3.7.4. Wavelet CLEAN
  • 3.7.5. Multiscale Entropy
  • 3.8. Deconvolution and Resolution
  • 3.9. Super-Resolution
  • 3.9.1. Definition
  • 3.9.2. Gerchberg-Saxon-Papoulis Method
  • 3.9.3. Deconvolution with Interpolation
  • 3.9.4. Undersampled Point Spread Function
  • 3.9.5. Multiscale Support Constraint
  • 3.10. Conclusions and Summary
  • 4. Detection
  • 4.1. Introduction
  • 4.2. From Images to Catalogs
  • 4.3. Multiscale Vision Model
  • 4.3.1. Introduction
  • 4.3.2. Multiscale Vision Model Definition
  • 4.3.3. From Wavelet Coefficients to Object Identification
  • 4.3.4. Partial Reconstruction
  • 4.3.5. Examples
  • 4.3.6. Application to ISOCAM Data Calibration
  • 4.4. Detection and Deconvolution
  • 4.5. Conclusion
  • 4.6. Summary
  • 5. Image Compression
  • 5.1. Introduction
  • 5.2. Lossy Image Compression Methods
  • 5.2.1. The Principle
  • 5.2.2. Compression with Pyramidal Median Transform
  • 5.2.3. PMT and Image Compression
  • 5.2.4. Compression Packages
  • 5.2.5. Remarks on These Methods
  • 5.3. Comparison
  • 5.3.1. Quality Assessment
  • 5.3.2. Visual Quality
  • 5.3.3. First Aladin Project Study
  • 5.3.4. Second Aladin Project Study
  • 5.3.5. Computation Time
  • 5.3.6. Conclusion
  • 5.4. Loss less Image Compression
  • 5.4.1. Introduction
  • 5.4.2. The Lifting Scheme
  • 5.4.3. Comparison
  • 5.5. Large Images: Compression and Visualization
  • 5.5.1. Large Image Visualization Environment: LIVE
  • 5.5.2. Decompression by Scale and by Region
  • 5.5.3. TheSAO-DS9 LIVE Implementation
  • 5.6. Summary
  • 6. Multichannel Data
  • 6.1. Introduction
  • 6.2. The Wavelet-Karhunen-Loeve Transform
  • 6.2.1. Definition
  • 6.2.2. Correlation Matrix and Noise Modeling
  • 6.2.3. Scale and Karhunen-Loaeve Transform
  • 6.2.4. The WT-KLT Transform
  • 6.2.5. TheWT-KLT Reconstruction Algorithm
  • 6.3. Noise Modeling in the WT-KLT Space
  • 6.4. Multichannel Data Filtering
  • 6.4.1. Introduction
  • 6.4.2. Reconstruction from a Subset of Eigenvectors
  • 6.4.3. WT-KLT Coefficient Thresholding
  • 6.4.4. Example: Astronomical Source Detection
  • 6.5. The Haar-Multichannel Transform
  • 6.6. Independent Component Analysis
  • 6.7. Summary
  • 7. An Entropic Tour of Astronomical Data Analysis
  • 7.1. Introduction
  • 7.2. The Concept of Entropy
  • 7.3. Multiscale Entropy
  • 7.3.1. Definition
  • 7.3.2. Signal and Noise Information
  • 7.4. Multiscale Entropy Filtering
  • 7.4.1. Filtering
  • 7.4.2. The Regularization Parameter
  • 7.4.3. Use of a Model
  • 7.4.4. The Multiscale Entropy Filtering Algorithm
  • 7.4.5. Optimization
  • 7.4.6. Examples
  • 7.5. Deconvolution
  • 7.5.1. The Principle
  • 7.5.2. The Parameters
  • 7.5.3. Examples
  • 7.6. Multichannel Data Filtering
  • 7.7. Background Fluctuation Analysis
  • 7.8. Relevant Information in an Image
  • 7.9. Multiscale Entropy and Optimal Compressibility
  • 7.10. Conclusions and Summary
  • 8. Astronomical Catalog Analysis
  • 8.1. Introduction
  • 8.2. Two-Point Correlation Function
  • 8.2.1. Introduction
  • 8.2.2. Determiningthe2-PointCorrelationFunction
  • 8.2.3. Error Analysis
  • 8.2.4. Correlation Length Determination
  • 8.2.5. Creation of Random Catalogs
  • 8.2.6. Examples
  • 8.3. Fractal Analysis
  • 8.3.1. Introduction
  • 8.3.2. The Hausdorff and Minkowski Measures
  • 8.3.3. The Hausdorff and Minkowski Dimensions
  • 8.3.4. Multifractality
  • 8.3.5. Generalized Fractal Dimension
  • 8.3.6. Wavelet and Multifractality
  • 8.4. Spanning Trees and Graph Clustering
  • 8.5. Voronoi Tessellation and Percolation
  • 8.6. Model-Based Clustering
  • 8.6.1. Modeling of Signal and Noise
  • 8.6.2. Application to Thresholding
  • 8.7. Wavelet Analysis
  • 8.8. Nearest Neighbor Clutter Removal
  • 8.9. Summary
  • 9. Multiple Resolution in Data Storage and Retrieval
  • 9.1. Introduction
  • 9.2. Wavelets in Database Management
  • 9.3. Fast Cluster Analysis
  • 9.4. Nearest Neighbor Finding on Graphs
  • 9.5. Cluster-Based User Interfaces
  • 9.6. Images from Data
  • 9.6.1. Matrix Sequencing
  • 9.6.2. Filtering Hypertext
  • 9.6.3. Clustering Document-TermData
  • 9.7. Summary
  • 10. Towards the Virtual Observatory
  • 10.1. Data and Information
  • 10.2. The Information Handling Challenges Facing Us
  • Appendix A. à Trous Wavelet Transform
  • Appendix B. Picard Iteration
  • Appendix C. Wavelet Transform Using the Fourier Transform
  • Appendix D. Derivative Needed for the Minimization
  • Appendix E. Generalization of the Derivative Needed for the Minimization
  • Appendix F. Software and Related Developments
  • Bibliography
  • Index
The Sumner Mayburg Library Fund, established with a bequest from her estate bookplate
The John Crerar Library bookplate

Similar Items