數位影像在傳輸時可能受到脈衝雜訊攻擊，降低了影像品質，而本論文提出一個可調式中值濾波器(switching median filter)，以期濾除高密度雜訊影像之後還能保有影像的品質，以及有效的偵測出雜訊位置。而偵測雜訊的方法是改進2006年IEEE期刊論文提出的BDND(Boundary Discriminative Noise Detection)演算法，改良BDND在高強度脈衝雜訊(high intensity impulse noise)與低強度脈衝雜訊(low intensity impulse noise)密度比例不同的情況產生的雜訊偵測錯誤，此外還提出在雜訊範圍擴大後還能正確的判斷雜訊點的方法。最後將一個雜訊影像中判斷為雜訊的點以有效點標準中値濾波器進行雜訊濾除以達到最好的效果。而本論文著重於精確的找出雜訊點以提高濾波品質。本論文以四種雜訊模型下的濾波結果來評估表現，發現所提出的修正BDND演算法在濾除這四種雜訊模型下的雜訊影像有很好的表現。
關鍵字：影像雜訊濾除、脈衝雜訊偵測、非線性濾波器、可調式中值濾波器

Digital images could be contaminated by impulse noise during image transmission. It could severely degrade the image quality. A switching median filter is proposed in this thesis for effectively denoising extremely corrupted images and preserving images detail and determining whether the current pixel is corrupted. The proposed method is based on the BDND (Boundary Discriminative Noise Detection) algorithm. We modify its defects on unequal densities of “low-intensity impulse noise” and “high-intensity impulse noise” and provide novel noise detection techniques for noise image with the corruption range of both low-intensity and high-intensity noise. We use standard median filter and only consider the uncorrupted pixels to filter out the noise. Four noise models are considered for performance evaluation. The result can clearly show that our proposed modified BDND attains good performance and improves image quality.

Index Items－Image denoising, impulse noise detection, nonlinear filter, switching median filter.

參考文獻
[1] I. Pitas, A. N. Venetsanopoulos, “Order statistics in digital image processing,” Proc. IEEE, vol. 80, no.12 pp. 1983–1921, Dec. 1992.
[2] D. R. K. Brownrigg, “The weighted median filters,” Commun. ACM, vol. 27, no. 8, pp. 807–818, Aug. 1984.
[3] S.-J. Ko and Y. H. Lee, “Center weighted median filters and their application to image enhancement,” IEEE Trans. Circuits Syst., vol. 38, no. 9, pp. 984–993, Sep. 1991.
[4] T. Sun and Y. Neuvo, “Detail-preserving median based filters in image processing,” Pattern Recognit. Lett., vol. 15, no. 4, pp. 341–347, Apr. 1994.
[5] D. A. Florencio and R. W. Schafer, “Decision-based median filter using local signal statistics,” in Proc. SPIE Vis. Commun. Image Process., vol. 2308, Sep. 1994, pp. 268–275.
[6] T. Chen, K.-K. Ma, and L.-H. Chen, “Tri-state median filter for image denoising,” IEEE Trans. Image Process., vol. 8, no. 12, pp. 1834–1838,Dec. 1999.
[7] Z. Wang and D. Zhang, “Progressive switching median filter for the removal of impulse noise from highly corrupted images,” IEEE Trans. Circuits Syst. II, vol. 46, no. 1, pp. 78–80, Jan. 1999.
[8] S. Zhang and M. A. Karim, “A new impulse detector for switching median filters,” IEEE Signal Process. Lett., vol. 9, no. 4, pp. 360–363, Nov. 2002.
[9] H.-L. Eng and K.-K. Ma, “Noise adaptive soft-switching median filter,” IEEE Trans. Image Process., vol. 10, no. 2, pp. 242–251, Feb. 2001.
[10] G. Pok, J.-C. Liu, and A. S. Nair, “Selective removal of impulse noise based on homogeneity level information,” IEEE Trans. Image Processing, vol. 12, no. 1,
pp. 85–92, Jan. 2003.
[11] H. Hwang and R. A. Haddad, “Adaptive median filters: New algorithms and results,” IEEE Trans. Image Process., vol. 4, no. 4, pp. 499–502, Apr. 1995.
[12] J.-S. Lim, Two-Dimensional Signal and Image Processing. Englewood Cliffs, NJ: Prentice-Hall, 1990.
[13] P.-E. Ng and K.-K. Ma, “A switching median filter with boundary discriminate noise detection for extremely corrupted images,” IEEE Trans. Image Process., vol. 15, no.6, pp.1506–1516, Jun. 2006.