在影像處理當中，清楚呈現邊緣，同時也去除不必要的雜訊是最基本的工作。當我們使用數位相機截取影像時，很常見的是彩色影像當中摻雜了各式各樣的雜訊，尤其是在低亮度的環境下使用高增益(ISO)值取得的影像。因此，雜訊濾波器在數位相機中是不可或缺的模組。而雜訊濾除的最大問題是會降低影像的銳利度，換句話說，光學鏡頭瑕疵對於影像的影響就有如低通濾波器一般，它會導致影像的模糊情形，習慣上，這樣的情形會應用邊緣增強演算法來改進影像的銳利度，但做邊緣增強的影像處理也常常會同時將雜訊的訊號也同時增強；所以，在做邊緣增強前，對於雜訊有效的濾除是非常重要的。在本論文中，我們將比較一個三邊濾波器與目前較常見濾波器的效能。論文中對三邊濾波器與常見濾波器做軟體模擬比較，也將此三邊濾波器的硬體以HDL語言實現，以便在影像處理晶片中使用。

Removing noise while preserving and enhancing edges is one of the most fundamental operations of image/video processing. When taking pictures with digital cameras, it is frequently found that the color images are corrupted by miscellaneous noise, especially images get with high ISO values in low luminance. Hence, noise filtering is a necessary module in digital still cameras. The difficulty of designing noise filter is that the filter will also reduce the sharpness of the image. On the other hand, optical lens imperfections are usually equivalent to spatial low pass filters and tend to result in blurred images. It is customary to apply edge enhancement algorithm on the image in order to improve the sharpness, but this process usually increase the noise level as a by-product. Hence, an efficient noise filter is very important before edge enhancement. In this paper, the efficiency of trilateral filter and other popular filters are compared briefly, and the trilateral filter is implemented by HDL language for image processing chip.

[1] R. C. Gonzalez and R. E. Woods, Digital image processing, Pearson Education, Upper Saddle River, New Jersey, 2002.
[2] http://inventors.about.com/library/inventors/bldigitalcamera.htm
[3] T. Chen and H. R. Wu, “Space variant median filters for the restoration of impulse noise corrupted images,” IEEE Trans. Circuit and Systems, vol. 48, no. 8, pp. 784-789, Aug. 2001.
[4] L. R. Rabiner, M. R. Sambur, and C. E. Schmidt, ”Applications of a nonlinear smoothing algorithm to speech processing,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-23, PP. 552-557, Sec. 1975.
[5] N. S. Jayant, “Average and median-based smoothing techniques for improving digital speech quality in the presence of transmission errors,” IEEE Trans. Commun., vol. COM-24, pp. 1043-1045, Sep. 1976.
[6] W. K. Pratt, “Median filtering,” in Semiannual Report, Image Processing Institute, Univ. of Southern California, pp. 116-123, Sep. 1975.
[7] B. R. Frieden, “A new restoring algorithm for the preferential enhancement of edge gradients,” J. Pot. Soc. Amer., vol. 66, pp. 280-283, 1976.
[8] L. Yin and R. Yang, “Circuits and systems exposition — weighted median filters: a tutorial,” IEEE Trans., Circuit and Systems, vol. 42, no. 10, pp. 583-601, Oct. 1995.
[9] R. Garnett, T. Huegerich, C. Chui, “A universal noise removal algorithm with an impulse detector“, IEEE Trans., image processing, vol. 14, no. 11, Nov. 2005.
[10] P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans., pattern analysis and machine intelligence, vol. 12, no. 7, Jul. 1990.
[11] C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proc. 6th Int. Conf. Computer Vision, New Delhi, India, pp. 839-846, 1998.
[12] P. S. Marc and J. S. Chen, “Adaptive smoothing: a general tool for early vision,” IEEE Trans., pattern analysis and machine intelligence, vol. 13, no. 6, Jun. 1991.
[13] R. Garnett, T. Huegerich, C. Chui, and W. He, “A universal noise removal algorithm with an impulse detector,” IEEE Trans. Image Processing, vol. 14, no. 11, pp.1747-1754, Nov. 2005.
[14] M. Basu, ”Gaussian-based edge-detection methods―a survey,” IEEE Trans., Systems, man, and cybernetics, vol. 32, no. 3, pp. 252-260, Aug. 2002.
[15] J. J. Francis and G. de Jager, “The bilateral median filter,” CiteSeer, 2003.
[16] H. C. Lee, Introduction to color image science, Cambridge University Press, 2005.
[17] W. C. Kao, H. S. Tai, C. P. Shen, J. A. Ye, and H. F. Ho, “A pipelined architecture design for trilateral noise filtering,” accepted for presenting in Proc. IEEE International Symposium Circuits and Systems (ISCAS), United States, May 2007.