Image Processing Based on Partial Differential Equations 1st Edition by Xue Cheng Tai, Knut Andreas Lie, Tony Chan, Stanley Osher 3540822399 978-3540822394 by Xue-cheng Tai, Knut-andreas Lie, Tony F. Chan, Stanley Osher 9783642069901, 3642069908 instant download after payment.

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Product details:

ISBN 10: 3540822399

ISBN 13: 978-3540822394

Author: Xue-Cheng Tai, Knut-Andreas Lie, Tony F. Chan, Stanley Osher

The book contains twenty-two original scienti?c research articles that address the state-of-the-art in using partial di?erential equations for image and signal processing. The articles arose from presentations given at the inter- tional conference on PDE-Based Image Processing and Related Inverse Pr- lems, held at the Centre of Mathematics for Applications, University of Oslo, Norway, August 8-12, 2005. The purpose of the conference was to bring together international - searchers to present various aspects of new developments in using numerical techniques for partial di?erential equations to analyse and process digital - ages. Various aspects of new trends and techniques in this ?eld were discussed in the conference, covering the following topics: • Level set methods and applications • Total variation regularization and other nonlinear ?lters • Noise analysis and removal • Image inpainting • Image dejittering • Optical ?ow estimation • Image segmentation • Image registration • Analysis and processing of MR images and brain mapping • Image construction techniques • Level set methods for inverse problems Inverse problems for partial di?erential equations have large areas of app- cations. Although image analysis and PDE inverse problems seem to be - related at a ?rst glance, there are many techniques used in one of these two areas that are useful for the other. One goal of the conference was to highlight some of the recent e?orts in merging some of the techniques for these two research areas.

Table of contents:

1. Image Inpainting Using a TV-Stokes Equation

2. Error Analysis for H1 Based Wavelet Interpolations

3. Image Dejittering Based on Slicing Moments

4. CLG Method for Optical Flow Estimation Based on Gradient Constancy Assumption

5. On Multigrids for Solving a Class of Improved Total Variation Based Staircasing Reduction Models

6. A Method for Total Variation-based Reconstruction of Noisy and Blurred Images

7. Minimization of an Edge-Preserving Regularization Functional by Conjugate Gradient Type Methods

8. A Newton-type Total Variation Diminishing Flow

9. Chromaticity Denoising using Solution to the Skorokhod Problem

10. Improved 3D Reconstruction of Interphase Chromosomes Based on Nonlinear Diffusion Filtering

11. Some Recent Developments in Variational Image Segmentation

12. Application of Non-Convex BV Regularization for Image Segmentation

13. Region-Based Variational Problems and Normal Alignment – Geometric Interpretation of Descent PDEs

14. Fast PCLSM with Newton Updating Algorithm

15. Nonlinear Multilevel Schemes for Solving the Total Variation Image Minimization Problem

16. Fast Implementation of Piecewise Constant Level Set Methods

17. The Multigrid Image Transform

18. Minimally Stochastic Schemes for Singular Diffusion Equations

19. Total Variation Based Image Registration

20. Variational Image Registration Allowing for Discontinuities in the Displacement Field

21. Shape Reconstruction from Two-Phase Incompressible Flow Data using Level Sets

22. Reservoir Description Using a Binary Level Set Approach with Additional Prior Information About the Reservoir Model

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Tags: Xue Cheng Tai, Knut Andreas Lie, Tony Chan, Stanley Osher, Differential