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Proceedings Paper

Content-adaptive mesh modeling for image restoration
Author(s): Yongyi Yang; Jovan G. Brankov; Nikolas P. Galatsanos
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Paper Abstract

In this work we explore the use of a content-adaptive mesh model (CAMM) in the classical problem of image restoration. In the proposed framework, we first model the image to be restored by an efficient mesh representation. A CAMM can be viewed as a form of image representation using non-uniform samples, of which the mesh nodes (i.e., image samples) are adaptively placed according to the local content of the image. The image is then restored through estimating the model parameters (i.e., mesh nodal values) from the data. There are several potential advantages of the proposed approach. First, a CAMM provides a spatially-adaptive regularization framework. This is achieved by the fact that the interpolation basis functions in a CAMM have support strictly limited to only those elements that they are associated with. Second, a CAMM provides an efficient, but accurate, representation of the image, thereby greatly reducing the number of parameters to be estimated. In this work we present some exploratory results to demonstrate the proposed approach.

Paper Details

Date Published: 1 July 2003
PDF: 12 pages
Proc. SPIE 5016, Computational Imaging, (1 July 2003); doi: 10.1117/12.479702
Show Author Affiliations
Yongyi Yang, Illinois Institute of Technology (United States)
Jovan G. Brankov, Illinois Institute of Technology (United States)
Nikolas P. Galatsanos, Illinois Institute of Technology (United States)

Published in SPIE Proceedings Vol. 5016:
Computational Imaging
Charles A. Bouman; Robert L. Stevenson, Editor(s)

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