基于Zernike 矩相似度联合的图像滤波
Image Denoising by Zernike-Moment-Similarity Collaborative Filtering

作者: 肖秀春 :中山大学信息科学与技术学院、广东海洋大学信息学院; 赖剑煌 :中山大学信息科学与技术学院,广州;

关键词: 图像去噪多边滤波非局域均值滤波相似性测度Zernike 矩Image Denoising Multilateral Filtering Non-Local Means Filtering Similarity Measure Zernike Moments

摘要:

非局域均值滤波(non-local means filtering, NLMF)采用图像块间灰度差测度像素间相似性,由于灰度差

Abstract: 易受噪声影响,这种相似性测度缺乏鲁棒性。图像块的Zernike 矩是块内像素灰度的统计量,且具有旋转无关特

Abstract: 性,能在抑制噪声的情况下较好地描述图像块特征。由图像块的各阶Zernike 矩差代替灰度差可定义Zernike 矩

Abstract: 相似度;联合各阶Zernike 矩相似度经加权平均可估计出所处理像素的灰度。仿真实验及分析表明文中算法相比

Abstract: 直接采用灰度差定义相似度的算法,能更好地去除噪声,获得更高的峰值信噪比(PSNR)。

Because similarity function defined in non-local means filter is subject to image noise, it cannot robustly

Abstract: represent the real similarity between pixels. Zernike moments are good statistics of the pixels in image patch, and have

Abstract: rotation-invariant feature, so they can be utilized to describe image feature while resistance to noise. In this paper,

Abstract: Zernike-moment-similarity is defined according to the difference of Zernike moments instead of pixel intensity, and

Abstract: then the intensity of the processed pixel is estimated by weighting the intensities of the local window according to the

Abstract: collaborative Zernike-moment-similarity. Simulation experiment results and analysis demonstrate that the presented

Abstract: algorithm can achieve better performance and higher PSNR than the current algorithms which directly adopting intensity

Abstract: difference as its similarity function.

 

 

文章引用: 肖秀春 , 赖剑煌 (2013) 基于Zernike 矩相似度联合的图像滤波。 图像与信号处理, 2, 1-7. doi: 10.12677/JISP.2013.21001

参考文献

[1] 肖秀春, 彭群生 , 卢晓敏等 . 基于次序统计量像素灰度相似度的图像双边滤波 [J].计算机辅助设计与图形学学报 , 2011, 23(7): 1232-1237.

[2] 蔡泽民 , 赖剑煌 . 一种基于超完备字典学习的图像去噪方法 [J]. 电子学报, 2009, 37(2): 347-350.

[3] V. Aurich, J. Weule. Non-linear gaussian filters performing edge preserving diffusion. Proceedings of the DAGM Symposium, London, 1995: 538-545.

[4] S. M. Smith, J. M. Brady. SUSAN—A new approach to low level image processing. International Journal of Computer Vision, 1997, 23(1): 45-78.

[5] C. Tomasi, R. Manduchi. Bilateral filtering for gray and color images. Proceedings of the 1998 IEEE International Conference on Computer Vision, Bombay, 1998: 839-846.

[6] S. Fleishman, I. Drori and D. Cohen-Or. Bilateral mesh denois-ing. ACM Transactions on Graphics, 2003, 22(3): 950-953.

[7] H. Fan, Y. Yu and Q. Peng. Robust feature-preserving mesh de- noising based on consistent subneighborhoods. IEEE Transac-tions on Visualization and Computer Graphics, 2010, 16(2): 312-324.

[8] J. J. Francis, J. G. De. The bilateral median filter. Transactions of the South Africa Institute of Electrical Engineers, 2005, 96(2): 106-111.

[9] 张鑫, 王章野 , 范涵奇等 . 保特征的三维模型的三边滤波去噪算法[J].计算机辅助设计与图形学学报 , 2009, 21(7): 936- 942.

[10] A. Buades, B. Coll and N. Morel. A review of image denoising algorithms, with a new one. Multiscale Modeling and Simula-tion, 2005, 4(2): 490-530.

[11] A. Buades, B. Coll and J. M. Morel. A non-local algorithm for image denoising. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Washington, 2005: 60-65.

[12] T. Tasdizen. Principal neighborhood dictionaries for non-local means image denoising. IEEE Transactions on Image Processing, 2009, 18(12): 2649-2660.

[13] J. Orchard, M. Ebrahimi and A. Wong. Efficient nonlocal-means denoising using the SVD. Proceedings of IEEE International Con-ference on Image Processing, San Diego, 2008: 1732-1735.

[14] S. Zimmer, S. Didas and J. Weickert. A rotationally invariant block matching strategy improving image denoising with non- local means. Proceedings of International Workshop on Local and Non-local Approximation in Image Processing. Lausanne, 2008: 135-142.

[15] Y. Liu, J. Wang, X. Chen, et al. A robust and fast non-local means algorithm for image denoising. Journal of Computer Science and Technology, 2008, 23(4): 270-279.

[16] Z. Ji, Q. Chen, Q. Sun, et al. A moment-based nonlocal-means algorithm for image denoising. Information Processing Letters, 2009, 109(23-24): 1248-1244.

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