數位影像在傳送或接收的過程中，常因傳輸通道使影像受到脈衝雜訊的干擾，進而破壞影像中原有的資訊。因此，如何有效的移除影像中的雜訊，便成為一個很重要的研究課題。本論文中提出一個基於軟式切換(soft-switching)技術的濾波器方法，針對受到脈衝雜訊干擾的影像進行改善。濾波器架構包含了雜訊點偵測與濾波處理兩個階段。首先利用齊性層(homogeneous level)的概念，將影像中的所有像素分類為雜訊點像素與非雜訊點像素。針對雜訊點像素的部分，使用模糊推論方法，進一步將其區分成孤立雜訊點(isolated noise)像素、非孤立雜訊點(non-isolated noise)像素與邊界(edge)像素三種類型。孤立雜訊點與非孤立雜訊點則分別以標準中間值濾波器與改良式Peak-and-Valley濾波器來改善；而非雜訊點與邊界的部分不屬於雜訊，所以不做任何處理。由實驗結果顯示，本文所提的軟式切換濾波器，不僅能夠有效的移除影像中的脈衝雜訊，同時也可以保護影像的邊界與細節部分。

Digital images are often corrupted by impulse noise during image acquisition or transmission due to the interference of communication channels. How to efficiently remove the noise from the corrupted images has been an active research topic in the image processing community. In this thesis, a soft-switching technique has been developed to remove impulse noise from the corrupted image. The proposed method consists of two stages: noise detection and filtering. First, we use the concept of a homogeneous level to classify pixels into uncorrupted pixels and corrupted pixels. Then we further use a fuzzy technique to classify corrupted pixels into three categories: isolated impulse noise pixels, non-isolated impulse noise pixels and edge pixels. After finishing the noise detection step, we remove the isolated impulse noise and non-isolated impulse noise using a standard median filter and an improved Peak-and-Valley filter, respectively. The uncorrupted pixels and edge pixels are preserved without any modifications. According to our simulation results, the proposed scheme can suppress the impulse noise effectively and preserve the details and edges of images.

[1] B. M. Mehtre, N. N. Murthy, and S. Kapoor, “Segmentation of fingerprint using the directional image,＂Pattern Recognition, vol. 20, no. 4, pp. 429-435, 1987.
[2] Z. Pan, G. Healey, M. Prasad, and B. Tromberg, “Face recognition in hyperspectral images,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1552-1560, Dec. 2003.
[3] H. Zhang and J. P. Ostrowski, “Visual motion planning for mobile robots,” IEEE Trans. on Robotics and Automation, vol. 18, no. 2, pp. 199-208, April 2002.
[4] Ｍ. Yu and Y. D. Kim, “An approach to Korean license plate recognition based on vertical edge matching,＂IEEE Int. Conf. on System, Man and Cybernetics, vol. 4, pp. 2975-2980, Oct. 2000.
[5] T. Chen and H. R. Wu, “Space variant median filters for the restoration of impulse noise corrupted images,” IEEE Trans. on Circuits and Systems, vol. 48, no. 8, pp. 784-789, Aug. 2001.
[6] R. C. Gonzalez and R. E. Woods, Digital Image Processing, New York: Addison-Wesley, 1992.
[7] O. Yli-Harja, J. Astola, and Y. Neuvo, “Analysis of the properties of median and weighted median filters using threshold logic and stack filter representation,” IEEE Trans. on Signal Processing, vol. 39, no. 2, pp. 395-410, Feb. 1991.
[8] S.-J. Ko and Y. H. Lee, “Center weighted median filters and their applications to image enhancement,” IEEE Trans. on Circuits and Systems, vol. 38, no. 9, pp. 984-993, Sep. 1991.
[9] T. Sun and Y. Neuvo, “Detail-preserving median based filters in image processing,” Pattern Recognition Lett., vol. 15, no. 4, pp. 341-347, 1994.
[10] Z. Wang and D. Zhang, “Progressive switching median filter for the removal of impulse noise from highly corrupted images,” IEEE Trans. on Circuits and Systems II, Exp. Briefs, vol. 46, no. 1, pp. 78-80, Jan. 1999.
[11] E. Abreu, M. Lightstone, S. K. Mitra, and K. Arakawa, “A new efficient approach for the removal of impulse noise from highly corrupted images,” IEEE Trans. on Image Processing, vol. 5, no. 6, pp. 1012-1025, June 1996.
[12] T. Chen, K.-K. Ma, and L.-H. Chen, “Tri-state median filter for image denoising,” IEEE Trans. on Image Processing, vol. 8, no. 12, pp. 1834-1838, Dec. 1999.
[13] I. Andreadis and G. Louverdis, “Real-time adaptive image impulse noise suppression,” IEEE Trans. on Instrumentation and Measurement, vol. 53, no. 3, pp. 798-806, June 2004.
[14] V. Crnojevic, V. Senk, and Z. Trpovski, “Advance impulse detection based on pixel-wise MAD,” IEEE Signal Processing Lett., vol. 11, no. 7, July 2004.
[15] A. Taguchi, “A design method of fuzzy weighted median filter,” IEEE Int. Conf. on Image Processing, vol. 1, pp. 423-426, Sept. 1996.
[16] Y. H. Kuo, C. S. Lee, and C. L. Chen, “High-stability AWMF filter for signal restoration and its hardware design,” Fuzzy Sets and Systems, vol. 114, no. 2, pp. 185-202, 2000.
[17] J. H. Wang, W. J. Liu, and L. D. Lin, “Histogram-based fuzzy filter for image restoration,” IEEE Trans. on System, Man and Cybernetics, Part B, vol. 32, no. 2, pp. 230-238, April, 2002.
[18] Y. Choi and R. Krishnapuram, “A robust approach to image enhancement based on fuzzy logic,” IEEE Trans. on Image Processing, vol. 6, pp. 808-825, June 1997.
[19] M. Doroodchi and A. M. Reza, “Fuzzy cluster filter,” IEEE Int. Conf. on Image Processing, vol. 2, pp. 939-942, Sept. 1996.
[20] H. L. Eng and K. K. Ma, “Noise adaptive soft-switching median filter,” IEEE Trans. on Image Processing, vol. 10, pp. 242-251, Feb. 2001.
[21] 陳孝同、張真誠、黃國峰，數位影像處理技術，旗標，台北，民93。
[22] 連國珍，數位影像處理，儒林，台北，民92。
[23] 謬紹綱，數位影像處理，第二版，培生，台北，民92。
[24] T. Chen and H. R. Wu, “Adaptive impulse detection using center-weighted median filter,” IEEE Signal Processing Lett., vol. 8, no. 1, pp. 1-3, Jan. 2001.
[25] G. Pok, J.-C. Liu, and A. S. Nair, “Selective removal of impulse noise based on homogeneity level information,” IEEE Trans. on Image Processing, vol. 12, no. 1, Jan. 2003
[26] S. Zhang and M. A. Karim, “A new impulse detector for switching median filters,” IEEE Signal Processing Lett., vol. 9, no. 11, pp. 360-363, Nov. 2002.
[27] E. Abreu and S. K. Mitra, “A signal-dependent rank ordered mean (SD-ROM) filter-a new approach for removal of impulses from highly corrupted images,” in Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing, vol. 4, pp. 2371-2374, May 1995.
[28] P. S. Windyga, “Fast impulsive noise removal,” IEEE Trans. on Image Processing, vol. 10, no. 1, pp. 173-179, Jan. 2001.
[29] 鍾國亮，數位影像處理與電腦視覺，東華，台北，民91。
[30] R. H. Chan, C.-W. Ho, and M. Nikolova, “Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization.” IEEE Trans. on Image Processing, vol. 14, no. 10, pp. 1479-1485, Oct. 2005.
[31] N. Alajlan, M Kamel, and E. Jernigan, “Detail preserving impulse noise removal,” Signal Processing on Image Communication, vol. 19, pp. 993-1003, 2004.
[32] D. V. D. Ville, M. Nachtegael, D. V. D, Weken, E. E. Kerre, W. Philips, and I. Lemahieu, “Noise reduction by fuzzy image filtering,” IEEE Trans. on Fuzzy System, vol. 11, no.4, pp. 429-436, Aug. 2003.