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Proceedings Paper

An adaptive M-estimation framework for robust image super resolution without regularization
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Paper Abstract

This paper introduces a new image super-resolution algorithm in an adaptive, robust M-estimation framework. Super-resolution reconstruction is formulated as an optimization (minimization) problem whose objective function is based on a robust error norm. The effectiveness of the proposed scheme lies in the selection of a specific class of robust M-estimators, redescending M-estimators, and the incorporation of a similarity measure to adapt the estimation process to each of the low-resolution frames. Such a choice helps in dealing with violations to the assumed imaging model that could have generated the low-resolution frames from the unknown high-resolution one. The proposed approach effectively suppresses the outliers without the use of regularization in the objective function, and results in high-resolution images with crisp details and no artifacts. Experiments on both synthetic and real sequences demonstrate the superior performance over methods based on the L2 and L1 in the objective function.

Paper Details

Date Published: 28 January 2008
PDF: 12 pages
Proc. SPIE 6822, Visual Communications and Image Processing 2008, 68221D (28 January 2008); doi: 10.1117/12.767020
Show Author Affiliations
Noha A. El-Yamany, Southern Methodist Univ. (United States)
Panos E. Papamichalis, Southern Methodist Univ. (United States)

Published in SPIE Proceedings Vol. 6822:
Visual Communications and Image Processing 2008
William A. Pearlman; John W. Woods; Ligang Lu, Editor(s)

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