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- W2034306717 abstract "This paper proposes a novel image super-resolution (SR) algorithm in a robust estimation framework. SR estimation is formulated as an optimization (minimization) problem whose objective function is based on robust M-estimators and its solution yields the SR output. The novelty of the proposed scheme lies in the selection of this class of estimators and the incorporation of information-theoretic similarity measures. Such a choice helps in dealing with violations (outliers) of the assumed mathematical model that generated the low-resolution images from the unknown high-resolution one. The proposed approach results in high-resolution images with no estimation artifacts. Experimental results demonstrate its superior performance in comparison to both L <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> and L <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> estimation in terms of robustness and speed of convergence." @default.
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- W2034306717 date "2007-04-01" @default.
- W2034306717 modified "2023-09-27" @default.
- W2034306717 title "A Robust Image Super-Resolution Scheme Based on Redescending M-Estimators and Information-Theoretic Divergence" @default.
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- W2034306717 doi "https://doi.org/10.1109/icassp.2007.366014" @default.
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