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Bayesian Inpainting Based on Geometric Image Models

In: Recent Progress in Computational and Applied PDES

Author

Listed:
  • Tony F. Chan

    (UCLA, Department of Mathematics)

  • Jianhong Shen

    (University of Minnesota, School of Mathematics)

Abstract

Image inpainting is an image restoration problem, with wide applications in image processing, vision analysis, and the movie industry. This paper surveys and summarizes all the recent inpainting models based on the Bayesian and variational principle. A unified view is developed around the central topic of geometric image models. We also discuss their associated Euler-Lagrange PDE’s and numerical implementation. A few open problems are proposed.

Suggested Citation

  • Tony F. Chan & Jianhong Shen, 2002. "Bayesian Inpainting Based on Geometric Image Models," Springer Books, in: Tony F. Chan & Yunqing Huang & Tao Tang & Jinchao Xu & Long-An Ying (ed.), Recent Progress in Computational and Applied PDES, pages 73-99, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4615-0113-8_5
    DOI: 10.1007/978-1-4615-0113-8_5
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