- Volume 23 Issue 1
DOI QR Code
Support Vector Machine and Improved Adaptive Median Filtering for Impulse Noise Removal from Images
영상에서 Support Vector Machine과 개선된 Adaptive Median 필터를 이용한 임펄스 잡음 제거
Lee, Dae-Geun;Park, Min-Jae;Kim, Jeong-Uk;Kim, Do-Yoon;Kim, Dong-Wook;Lim, Dong-Hoon
- Received : 20090800
- Accepted : 20090900
- Published : 2010.02.28
Images are often corrupted by impulse noise due to a noise sensor or channel transmission errors. The filter based on SVM(Support Vector Machine) and the improved adaptive median filtering is proposed to preserve image details while suppressing impulse noise for image restoration. Our approach uses an SVM impulse detector to judge whether the input pixel is noise. If a pixel is detected as a noisy pixel, the improved adaptive median filter is used to replace it. To demonstrate the performance of the proposed filter, extensive simulation experiments have been conducted under both salt-and-pepper and random-valued impulse noise models to compare our method with many other well known filters in the qualitative measure and quantitative measures such as PSNR and MAE. Experimental results indicate that the proposed filter performs significantly better than many other existing filters.
Support vector machine;improved Adaptive median filter;impulse noise;noise removal
- 이준희, 최어빈, 이원열, 임동훈 (2008). 영상에서 임펄스 잡음제거를 위한 적응력있는 가중 평균 필터, <응용통계연구>, 21, 233-245.
- Abreu, E. and Mitra, S. K. (1995). A signal-dependent rank ordered mean(SD-ROM) filter - a new approach for removal of impulses from highly corrupted images, IEEE Signal Processing, 4, 2371-2374.
- Chan, R. H., Ho, C. W. and Nikolova, M. (2005). Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization, IEEE Transactions on Image Processing, 14, 1479-1485. https://doi.org/10.1109/TIP.2005.852196
- Hwang, T. and Haddad, R. A. (1995). Adaptive median filters: New algorithms and results, IEEE Transactions on Image Processing, 4, 499-502. https://doi.org/10.1109/83.370679
- Ko, S. -J. and Lee, Y. -H. (1991). Center weighted median filters and their applications to image enhancement, IEEE Transactions on Circuits and Systems, 38, 984-993. https://doi.org/10.1109/31.83870
- Lim, D. H. (2006). Robust edge detection in noisy images, Computational Statistics and Data Analysis, 50, 803-812. https://doi.org/10.1016/j.csda.2004.10.005
- Lim, D. H. and Jang, S. J. (2002). Comparison of two-sample tests for edge detection in noisy images, Journal of Royal Statistical Society-The Statistician, 51, 21-30. https://doi.org/10.1111/1467-9884.00295
- Lin, T.-C. and Yu, P.-T. (2004). Adaptive two-pass median filter based on support vector machines for image restoration, Neural Computation, 16, 333-354. https://doi.org/10.1162/neco.2004.16.2.333
- Sun, T. and Neuvo, Y. (1994). Detail-preserving median based filters in image processing, Pattern Recognition Letters, 15, 341-347. https://doi.org/10.1016/0167-8655(94)90082-5
- Vapnik, V. (1998). The Nature of Statistical Learning Theory, Springer-Verlag, New York.