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基于多幅图像统计信息的降噪算法 被引量:3

Algorithm of Reducing Noise Based on Statistical Information of Multi-image
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摘要 讨论了基于多幅图像各象素的统计信息进行降噪的算法,在算法过程中,利用对同一景物采集的多幅图像,统计其各象素的不同的灰度值出现的次数,把得到的大概率值作为真实的灰度值,实现对图像的降噪处理。该算法处理后的图像噪声大大降低,轮廓更加清晰,并且能较好的保留原图像的细节。 The algorithm of reducing the noise based on the statistical information of the multi-image was discussed. On the course of the algorithm, the multi-image to the one scene was used to count the values of gray level of each pixel; the greater probability was the true value of gray level, so the image was reduced noise. The noise of the image which was dealt with by this algorithm should reduce much, figure of the image should be more clarity, and the details of the primary image could be reserved well.
出处 《系统仿真学报》 CAS CSCD 北大核心 2006年第z1期383-384,共2页 Journal of System Simulation
基金 山东省自然科学基金(Y2002G07)
关键词 降噪 叠加处理 灰度值 reduce noise superposition technology value of gray level
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  • 1[1]W Danm,P Rose,H Heidt,et al.Automatic recognition of weld defects in X-ray inspection[J].British Journal of NDT,1987,29(2):79-82.
  • 2[2]B Eckelt,N Meyendorf,W Morgner,et al.Use of automatic image processing for monitoring of welding process and weld inspection[A].Proc.12th World Conf.on NDT[C],Amsterdam,1989.37-41.
  • 3[3]D Liang,W Zhen,G Zhang,et al.Automatical identification of the defect level of welding seam based on X-ray image[A].Proceedings of International Symposium on Nondestructive Testing and Stress-strain Measurement[C],Tokyo,1992.267-274.
  • 4[4]A Jain,M Dubuisson.Segmentation of X-ray and C-scan images of fiber reinforced composite materials[J].Pattern Recognition1992.25(3):257-270.
  • 5[5]R Murakami.Detection and classification of welding defects in the X-ray films by using image processing and neural network[A].IAPR QCAV98[C],1998.480-484.
  • 6[6]T W Liao,D M Li,Y M Li.Detection of welding flaws from radiographic images with fuzzy clustering methods[J].Fuzzy Sets and Systems,1999,108:145-158.
  • 7[7]V Lashkia.Defect detection in X-ray images using fuzzy reasoning[J].Image and Vision Computing,2001,19:261-269.

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