Bayesian Inpainting Based on Geometric Image Models

Tony F. Chan () and Jianhong Shen ()
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Tony F. Chan: UCLA, Department of Mathematics
Jianhong Shen: University of Minnesota, School of Mathematics

A chapter in Recent Progress in Computational and Applied PDES, 2002, pp 73-99 from Springer

Abstract: 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.

Keywords: Inpainting; interpolation; Bayesian; curve model; image model; Euclidean in-variance; elastica; bounded variation; Mumford-Shah; curvature; Γ-convergence; numerical PDE (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4615-0113-8_5

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DOI: 10.1007/978-1-4615-0113-8_5

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