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Defect denoising is a procedure to determine whether input defect data consist of any local defect clusters and, if local defects exist, to separate the local defects from the global defects. This can be done by “automatic defect detection”, [1] by using a variety of different methods:
         i) Spatial filter [1]. (see page4256)
         ii) Nearest-neighbor (NN) clutter removal method [2] (see page4255). This method is simple and easy of use of its tuning parameters.
         iii) Support vector clustering [3]. (see page4269)

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[1] C. H. Wang, W. Kuo, and H. Bensmial, “Detection and classification of defects patterns on semiconductor wafers,” IIE Trans., vol. 39, no. 12, pp. 1059–1069, 2006.
[2] S. Byers and A. E. Raftery, “Nearest-neighbor clutter removal for estimating features in spatial point processes,” J. Am. Stat. Assoc., vol. 93, no. 442, pp. 577–584, 1998.
[3] T. Yuan, S. J. Bae, and J. I. Park, “Bayesian spatial defect pattern recognition in semiconductor fabrication using support vector clustering,” Int. J. Adv. Manuf. Technol., vol. 51, pp. 671–683, Dec. 2010.