基于改进NL-means算法的显微CT图像降噪

李保磊; 杨 民; 李俊江

基于改进NL-means算法的显微CT图像降噪

北京航空航天大学机械工程及自动化学院, 北京 100191

详细信息

作者简介:
李保磊(1980-), 男, 山东菏泽人,博士生, baolei_li@yahoo.com.cn.

中图分类号: TP 391
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- 文章访问数: 3720
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- 被引次数: 0
出版历程
- 收稿日期: 2008-06-18
- 网络出版日期: 2009-07-31

Denoising of micro-CT image based on improved NL-means algorithm

School of Mechanical Engineering and Automation, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

摘要

摘要: 显微CT(Computed Tomography)采用微焦点射线源,射线剂量低,CT图像噪声大,对其降噪十分必要.综述了现存主要CT图像降噪算法及其优缺点,介绍了NL(nonlocal)-means算法,根据实验结果分析了其会在图像平滑区域引入人工伪影的不足.根据NL-means算法的不足,在原算法中引入图像的梯度信息,提出了改进的降噪算法,改进算法保持了原算法优良的降噪功能,并能有效抑制人工伪影,且能够提高图像细节对比度,实验结果验证了改进算法的有效性.
- 显微CT /
- 降噪 /
- 人工伪影 /
- 改进NL-means算法 /
- 对比度
Abstract: Micro-CT(computed tomography)is based on microfocus X-ray source and the ray dose is low. So the noise level of micro-CT image is big and denoising of the CT image is very necessary. The current main denoising methods for CT image and their advantages and disadvantages were summaried. The NL(nonlocal)-means algorithm was described. According to the experiment result, the disadvantage of NL-meas algorithm that artificial artifact will be introduced in smooth region of CT image was analyzed. Based on the shortage of the NL-means algorithm, gradient information of image was introduced in the original algorithm and the improved algorithm was put forward. The improved algorithm keeps the excellent denioising function and the artificial artifact was restrained effectively. The detail contrast can be also enhanced. The availability of the improved algorithm was validated by the experiment result.
- micro-CT(computed tomography) /
- denoising /
- artificial artifact /
- improved NL(nonlocal)-means algorithm /
- contrast

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参考文献(1)

[1] Li Yawwen, Liang Zhuang. A new method for nondestructive internal microtomography of IC and vacuum microelectronic devices Proceedings of 14th Int.Vacuum of Microelectronics Conference.Davis: IEEE,2001:15-16 [2] Wevers M, Meester P DE. Microfocus X-ray computer tomography in materials researh 15th World Conference of NDT. Roma:AIPnD,2000:899-906 [3] 李玉彬,李向良,姜燕梅.微焦点X射线CT及其在无损检测中的应用[J].无损检测,1999,21(12):549-552 Li Yubin, Li Xiangliang, Jiang Yanmei. X-ray computed microtomography and its application to NDT[J]. NDT,1999,21(12):549-552(in Chinese) [4] Li Tianfang, Li Xiang, Wang Jing, et al. Nonlinear sinogram smoothing for low-dose X-ray CT[J]. IEEE Transactions on Nuclear Science,2004,51(5): 2505-2513 [5] Lu H, Li X, Hsiao I T, et al. Analytical treatment of noise for low-dose CT projection data by penalized weighted least-square smoothing in the K-L domain[J]. SPIE Med Imag,2002,4682:146-152 [6] Demirkaya K. Reduction of noise and image artifacts in computed tomography by nonlinear filtration of the projection images[J]. SPIE Med Imag,2001,4322:917-923 [7] Hsieh J. Adaptive streak artifact reduction in computed tomography resulting from excessive X-ray photon noise[J]. Med Phys, 1998, 25:2139-2147 [8] Babaud J, Witkin A, Baudin M, et al. Uniqueness of the Gaussian kernel for scale space filtering[J]. IEEE Trans Pattern Anal Machine Intell, 1986, 8:26-33 [9] Lindenbaum, Fischer M, Bruckstein A M. On Gabor contribution to image enhancement [J]. Pattern Recognition, 1994, 27:1-8 [10] 阮秋琦.数字图像处理学[M].北京:电子工业出版社,2007 Ruan Qiuqi. Digital image processing[M]. Beijing: Publishing House of Electronics Industry,2007(in Chinese) [11] Rudin L, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D: Nonlinear Phenomena, 1992, 60(1-4):259-268 [12] Chang S G, Yu B, Vetterli M. Adaptive wavelet thresholding for image denoising and compression[J]. IEEE Transactions on Image Processing, 2000, 9(9):1532-1546 [13] Donoho D L. Denoising by soft-thresholding[J]. IEEE Transactions on Information Theory, 1995, 41(3):613-627 [14] Pan Q, Zhang L, Dai G, et al. Two denoising methods by wavelet transform [J]. IEEE Transactions on Signal Processing, 1999,47(12): 3401-3406 [15] Yaroslavsky L P. Local adaptive image restoration and enhancement with the use of DFT and DCT in a running window Wavelet Applications in Signal and Image Processing IV. Bellingham,WA:SPIE,1996,2825:1-13 [16] Smith S M, Brady J M. SUSAN:a new approach to low level image processing[J]International Journal of Computer Vision,1997,23(1),45-78 [17] Buades A, Coll B, Morel J M. Nonlocal image and movie denoising[J]. Int J Comput Vis,2008,76:123-139 [18] Buades A, Coll B, Morel J M. A review of image denoising algorithms, with a newone, multiscale model[J]. Simul,2005,4:490-530 [19] Buades A, Coll B, Morel J M. A non-local algorithm for image denoising IEEE International Conference on Computervision and Pattern Recognition.Washington DC:IEEE Computer Society, 2005,2:60-65

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