Abstract
In this paper, a novel adaptive filter ASBF based on support vector regression (SVR) is proposed to preserve more image details and efficiently suppress impulse noise simultaneously. The main idea of the novel filter ASBF here is to employ a SVR based impulse detector to judge whether an input pixel is contaminated or not by impulse noise. If this case happens, a median filter is employed to remove the corresponding impulse noise. This judgment procedure is executed by regressing the filter window of an input pixel using SVR and then judging the input pixel by its regression distance. Huber loss function is used in SVR regression, due to its excellent robustness capability. The distinctive advantage of the filter ASBF over the latest Support Vector Classifier (SVC) based filter is that no training for the original noise-free image is required in our approach, which is well in accordance with our visual judgment way. Experimental results for benchmark images demonstrate that our filter ASBF here outperforms the extensively-used median-based filters and the SVC based filter.
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References
Ko SJ, Lee YH (1991) Center weighted median filters and their applications to image enhancement. IEEE Trans Circ Syst 38:984–993
Arakawa K (1996) Median filters based on fuzzy rules and its application to image restoration. Fuzzy Set Syst 77:3–13
Chen T, Ma KK, Chen LH (1999) Tri-state median filter for image denoising. IEEE Trans Image Process 8:1834–1838
Tau-Chao Lin, Pao-Ta Yu (2004) Adaptive two median filter on support vector machines for image restoration. Neural Comput 16:333–354
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines. Cambridge University Press, London
Sonka M, Hlavac V, Boyle R (2002) Image processing, analysis, and machine vision, Second edition. Brooks/Cole, Belmont, CA, USA
Shitong W, Jiagang Z et al. Experimentally studies for the choice of parameters in support regression machine Norm-r_SVR. Soft Comput (accepted)
Shitong W, Jiagang Z et al. Theoretically optimal choice of parameters in support vector regression machines Huber_SVR and Norm-r_SVR. Soft Comput (accepted)
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Zhu, J., Wang, S., Wu, X. et al. A novel adaptive SVR based filter ASBF for image restoration. Soft Comput 10, 665–672 (2006). https://doi.org/10.1007/s00500-005-0536-2
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DOI: https://doi.org/10.1007/s00500-005-0536-2