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

Noha A. El-Yamany; Panos E. Papamichalis

doi:10.1117/12.767020

28 January 2008 An adaptive M-estimation framework for robust image super resolution without regularization

Noha A. El-Yamany, Panos E. Papamichalis

Proceedings Volume 6822, Visual Communications and Image Processing 2008; 68221D (2008) https://doi.org/10.1117/12.767020
Event: Electronic Imaging, 2008, San Jose, California, United States

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.

Citation Download Citation

Noha A. El-Yamany and Panos E. Papamichalis "An adaptive M-estimation framework for robust image super resolution without regularization", Proc. SPIE 6822, Visual Communications and Image Processing 2008, 68221D (28 January 2008); https://doi.org/10.1117/12.767020

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