Robust Image Denoising Using Kernel-Induced Measures

Authors:
Keren Tan;Songcan Chen;Daoqiang Zhang
Affiliations:
Nanjing University of Aeronautics & Astronautics, P.R. China;Nanjing University of Aeronautics & Astronautics, P.R. China;Nanjing University of Aeronautics & Astronautics, P.R. China
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Year:
2004

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Abstract

In this paper, we propose a class of novel nonlinear robust filters for image denoising by incorporating kernel -induced measures into classical linearmean filter. Particularly, we place more focus on Gaussian kernel based filter (GK) due to its simplicity. The GK filter not only generalizes and makes the original linear mean filter highly resistant to outliers but also outperforms a typical and powerful Mean-LogCauchy filter recently developed by Hamza et al in the mixed noise removal in certain specific conditions in the normalized mean square error (NMSE) sense. Also the experimental results illustrate that the kernel-based nonlinear filters are promising.