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Proceedings Paper

Image resampling and constraint formulation for multiframe superresolution restoration
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Paper Abstract

Multi-frame super-resolution restoration algorithms commonly utilize a linear observation model relating the recorded images to the unknown restored image estimates. Working within this framework, we demonstrate a method for generalizing the observation model to incorporate spatially varying point spread functions and general motion fields. The method utilizes results from image resampling theory which is shown to have equivalences with the multi-frame image observation model used in super-resolution restoration. An algorithm for computing the coefficients of the spatially varying observation filter is developed. Examples of the application of the proposed method are presented.

Paper Details

Date Published: 1 July 2003
PDF: 12 pages
Proc. SPIE 5016, Computational Imaging, (1 July 2003); doi: 10.1117/12.483906
Show Author Affiliations
Sean Borman, Univ. of Notre Dame (United States)
Robert L. Stevenson, Univ. of Notre Dame (United States)


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

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