Outlier robust corner-preserving methods for reconstructing noisy images

Martin Hillebrand; Christine H. Müller

doi:10.1214/009053606000001109

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arXiv · 0708.0481

Outlier robust corner-preserving methods for reconstructing noisy images

Martin Hillebrand·Christine H. Müller

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Abstract

The ability to remove a large amount of noise and the ability to preserve most structure are desirable properties of an image smoother. Unfortunately, they usually seem to be at odds with each other; one can only improve one property at the cost of the other. By combining M-smoothing and least-squares-trimming, the TM-smoother is introduced as a means to unify corner-preserving properties and outlier robustness. To identify edge- and corner-preserving properties, a new theory based on differential geometry is developed. Further, robustness concepts are transferred to image processing. In two examples, the TM-smoother outperforms other corner-preserving smoothers. A software package containing both the TM- and the M-smoother can be downloaded from the Internet.

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BibTeX RIS

Martin Hillebrand, Christine H. Müller. 2007-08-03. Outlier robust corner-preserving methods for reconstructing noisy images. https://doi.org/10.1214/009053606000001109

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