Abstract
In this paper, a novel adaptive filter based on support vector machines (SVMs) that preserves image details and effectively suppresses impulsive noise is proposed. The filter employs an SVM impulse detector to judge whether an input pixel is noisy. If a noisy pixel is detected, a median filter is triggered to replace it. Otherwise, it stays unchanged. To improve the quality of the restored image, an adaptive LUM filter based on scalar quantization (SQ) is activated. The optimal weights of the adaptive LUM filter are obtained using the least mean square (LMS) learning algorithm. Experimental results demonstrate that the proposed scheme outperforms other decision-based median filters in terms of noise suppression and detail preservation.
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References
Lin, T.-C., Yu, P.-T.: Partition fuzzy median filter based on fuzzy rules for image restoration. Fuzzy Sets and Systems 147, 75–97 (2004)
Chen, T., Ma, K.K., Chen, L.H.: Tri-state median filter for image denoising. IEEE Trans. Image Processing 8, 1834–1838 (1999)
Lin, T.-C., Yu, P.-T.: Adaptive two-pass median filter based on support vector machines for image restoration. Neural Computation 16, 333–354 (2004)
Arakawa, K.: Median filters based on fuzzy rules and its application to image restoration. Fuzzy Sets and Systems 77, 3–13 (1996)
Lin, T.-C.: Partition belief median filter based on Dempster-Shafer theory in image processing. Pattern Recognition 41, 139–151 (2008)
Smolka, B., Chydzinski, A.: Fast detection and impulsive noise removal in color images. Real-Time Imaging 11, 389–402 (2005)
Haykin, S.: Neural Networks A Comprehensive Foundation, 2nd edn. Prentice Hall, Englewood Cliffs (2005)
Lin, K.M., Lin, C.J.: A study on reduced support vector machines. IEEE Trans. Neural Networks 14, 1449–1459 (2003)
Lin, T.-C.: A new adaptive center weighted median filter for suppressing noise in images. Information Sciences 177, 1073–1087 (2007)
Lukac, R.: Binary LUM smoothing. IEEE Signal Processing Letters 19, 400–403 (2002)
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Lin, TC., Yeh, CT., Liu, MK. (2010). Application of SVM-Based Filter Using LMS Learning Algorithm for Image Denoising. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_11
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DOI: https://doi.org/10.1007/978-3-642-17534-3_11
Publisher Name: Springer, Berlin, Heidelberg
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