
- 564 pages
- English
- PDF
- Available on iOS & Android
Image Recovery: Theory and Application
About this book
Image Recovery: Theory and Application focuses on signal recovery and synthesis problems. This book discusses the concepts of image recovery, including regularization, the projection theorem, and the pseudoinverse operator. Comprised of 13 chapters, this volume begins with a review of the basic properties of linear vector spaces and associated operators, followed by a discussion on the Gerchberg-Papoulis algorithm. It then explores image restoration and the basic mathematical theory in image restoration problems. The reader is also introduced to the problem of obtaining artifact-free computed tomographic reconstruction. Other chapters consider the importance of Bayesian approach in the context of medical imaging. In addition, the book discusses the linear programming method, which is particularly important for images with large number of pixels with zero value. Such images are usually found in medical imaging, microscopy, electron microscopy, and astronomy. This book can be a valuable resource to materials scientists, engineers, computed tomography technologists, and astronomers.
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Table of contents
- Front Cover
- Image Recovery: Theory and Application
- Copyright Page
- Table of Contents
- Preface
- Acknowledgments
- Chapter 1. Signal Restoration, Functional Analysis, and Fredholm Integral Equations of the First Kind
- Chapter 2. Mathematical Theory of Image Restoration by the Method of Convex Projections
- Chapter 3. Bayesian and Related Methods in Image Reconstruction from Incomplete Data
- Chapter 4. Image Restoration Using Linear Programming
- Chapter 5. The Principle of Maximum Entropy in Image Recovey
- Chapter 6. The Unique Reconstruction of Multidimensional Sequences from Fourier Transform Magnitude or Phase
- Chapter 7. Phase Retrieval and Image Reconstruction for Astronomy
- Chapter 8. Restoration from Phase and Magnitude by Generalized Projections
- Chapter 9. Image Reconstruction from Limited Data: Theory and Applications in Computerized Tomography
- Chapter 10. Computer-Assisted Diffraction Tomography
- Chapter 11. Applications of Convex Projection Theory to Image Recovery in Tomography and Related Areas
- Chapter 12. Image Synthesis: Discovery Instead of Recovery
- Chapter 13. The Role of Analyticity in Image Recovery
- Index