Myriad-based shift parameter estimation method and its application to image filtering and processing

Vladimir V. Lukin; Sergey K. Abramov; Alexander A. Zelensky; Jaakko T. Astola

doi:10.1117/12.614829

30 August 2005 Myriad based shift parameter estimation method and its application to image filtering and processing

Vladimir V. Lukin, Sergey K. Abramov, Alexander A. Zelensky, Jaakko T. Astola

Proceedings Volume 5916, Mathematical Methods in Pattern and Image Analysis; 591601 (2005) https://doi.org/10.1117/12.614829
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

Abstract

One typical problem in image processing is to remove noise. This problem becomes rather complicated if not one type of noise is present, i.e. if, for example, an image is corrupted by mixed noise. Non-adaptive nonlinear filters often do not provide desirable image processing quality. Nonlinear locally adaptive hard-switching filters are more flexible and effi-cient in this sense. However, for mixed noise case, all component filters employed in locally adaptive filtering (LAF) framework have to possess robust properties. In particular, this relates to the so-called noise suppressing filter (NSF) to be applied in image homogeneous regions. Below we show that myriad filter with properly selected tunable (lineariza-tion) parameter is able to be an efficient NSF that outperforms well known α-trimmed and Wilcoxon filters. For this purpose, general statistical analysis for myriad estimate of distribution shift (location) parameter is performed first. Then numerical simulation results for myriad estimator are obtained and compared to other robust shift parameter estimators for different parameters of data sample model used. The recommendations for tunable parameter selection are given. The proposed filtering technique is verified for both artificial and real images.

Citation Download Citation

Vladimir V. Lukin, Sergey K. Abramov, Alexander A. Zelensky, and Jaakko T. Astola "Myriad based shift parameter estimation method and its application to image filtering and processing", Proc. SPIE 5916, Mathematical Methods in Pattern and Image Analysis, 591601 (30 August 2005); https://doi.org/10.1117/12.614829

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