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Image Processing Based on Partial Differential Equations

Proceedings of the International Conference on PDE-Based Image Processing and Related Inverse Problems, CMA, Oslo, August 8-12, 2005

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

Image Processing Based on Partial Differential Equations Cover Image

  • ISBN13: 978-3-5403-3266-4
  • 450 Pages
  • User Level: Science
  • Publication Date: November 22, 2006
  • Available eBook Formats: PDF
  • eBook Price: $159.00
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Full Description
This book publishes a collection of original scientific research articles that address the state-of-art in using partial differential equations for image and signal processing. The topics covered in this book include: level set methods for image segmentation and construction, denoising techniques, digital image inpainting, image dejittering, image registration, and fast numerical algorithms for solving these problems. The book is suitable for readers working with computer vision and visualization, image and signal processing, as well as medical imaging and numerical mathematics. The partial differential equations used for different problems discussed in this proceeding provide some rich research topics for people working with mathematical analysis and numerical simulations. This volume collects new developments in this field and points to the newest literature results. It is good resource for people working on related problems as well as for people who are new in this field.
Table of Contents

Table of Contents

  1. From the contents Part I Digital Image Inpainting, Image Dejittering, and Optical Flow Estimation. Image Inpainting Using a TV
  2. Stokes Equation
  3. Xue
  4. Cheng Tai, Stanley Osher, Randi Holm. Error Analysis for H1 Based Wavelet Interpolations
  5. Tony F. Chan, Hao
  6. Min Zhou, Tie Zhou. Image Dejittering Based on Slicing Moments
  7. Sung Ha Kang, Jianhong (Jackie) Shen. CLG Method for Optical Flow Estimation Based on Gradient Constancy Assumption – Adam Rabcewicz.
  8. Part II Denoising and Total Variation Methods. On Multigrids for Solving a Class of Improved Total Variation Based Staircasing Reduction Models
  9. Joseph Savage, Ke Chen. A Method for Total Variation
  10. based Reconstruction of Noisy and Blurred Images
  11. Qianshun Chang, Weicheng Wang, Jing Xu. Minimization of an Edge
  12. Preserving Regularization Functional by Conjugate Gradient Type Methods
  13. Jian
  14. Feng Cai, Raymond H. Chan, Benedetta Morini. A Newton
  15. type Total Variation Diminishing Flow
  16. Wolfgang Ring. Chromaticity Denoising using Solution to the Skorokhod Problem
  17. Dariusz Borkowski. Improved 3D Reconstruction of Interphase Chromosomes Based on Nonlinear Diffusion Filtering
  18. Jan Huben´y, Pavel Matula, Petr Matula, Michal Kozubek.
  19. Part III Image Segmentation. Application of Non
  20. Convex BV Regularization for Image Segmentation
  21. Klaus Frick, Otmar Scherzer. Region
  22. Based Variational Problems and Normal Alignment –Geometric Interpretation of Descent PDEs
  23. Jan Erik Solem, Niels Chr. Overgaard. Fast PCLSM with Newton Updating Algorithm
  24. Xue
  25. Cheng Tai, Chang
  26. Hui Yao.
  27. Part IV Fast Numerical Methods.
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