SAR image despeckling based on adaptive non-local means

被引：1
作者：
Chen S. [1 ]
Li X. [2 ]
机构：
[1] Colledge of Postgraduate, Academy of Equipment, Beijing
[2] Department of Space Equipment, Academy of Equipment, Beijing
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2017年 / 39卷 / 12期
关键词：
Adaptive block matching; Block similarity measure; Gabor filter; Synthetic aperture radar (SAR);
D O I：
10.3969/j.issn.1001-506X.2017.12.08
中图分类号：
学科分类号：
摘要：
To deal with the shortage of inaccuracy and low-level robustness of block similarity measure for synthetic aperture radar (SAR) image despeckling using the traditional non-local means method, a new SAR image despeckling method based on adaptive non-local means is proposed. Firstly, the smoothness which is defined to measure image texture complexity is used to design the matching functions, and on the basis of those functions the size of image block and search window can be adjusted adaptively in order to improve the accuracy of block similarity measure. As a result, a framework of the adaptive non-local algorithm is proposed. Secondly, the block similarity is measured by the Gabor filter for enhancing the robustness of block similarity measure, and an adaptive non-local means method based on Gabor filter is presented when combined with the proposed framework. The experiment results show that the proposed method can not only reduce speckle efficiently, but also preserve the texture, edges and targets well, which lies the foundations for the understanding and interpretation of SAR images. © 2017, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：2683 / 2690
页数：7
相关论文
共 16 条
  • [1] Wang Y., Ainsworth T.L., Lee J., On characterizing high-resolution SAR imagery using kernel-based mixture speckle models, IEEE Geoscience and Remote Sensing Letters, 12, 5, pp. 968-972, (2015)
  • [2] Wu J., Liu F., Jiao L.C., Local maximal homogeneous region search for SAR speckle reduction with sketch-based geometrical kernel function, IEEE Trans. on Geoscience and Remote Sensing, 52, 9, pp. 5751-5764, (2014)
  • [3] Jiang Y., Zhang X.L., Shi J., Speckle reduction for polarimetric SAR images by improved Lee filter, Journal of University of Electronic Science and Technology of China, 38, 1, pp. 5-8, (2009)
  • [4] Chen G., Liu X., Contourlet-based despeckling for SAR image using hidden Markov tree and Gaussian Markov models, Proc. of the Asian and Pacific Conference on Sythetic Aperture Radar, pp. 784-787, (2007)
  • [5] Buades A., Coll B., Morel J., A non-local algorithm for image denoising, Computer Vision and Pattern Recognition, 2, 7, pp. 60-65, (2005)
  • [6] Deledalle C.A., Denis L., Tupin F., Iterative weighted maximum likehood denoising with probabilistic patch-based weights, IEEE Trans. on Image Processing, 18, 12, pp. 2661-2672, (2009)
  • [7] Yi Z.L., Yin D., Hu A.Z., Et al., SAR image despeckling based on non-local means filter, Journal of Electronics and Information Technology, 34, 4, pp. 950-955, (2012)
  • [8] Yan X.Y., Jiao L.C., SAR image despeckling via modified non-local means: based on sure criterion, Journal of University of Electronic Science and Technology of China, 43, 1, pp. 42-48, (2014)
  • [9] Zhang X.H., Chen J.W., Meng H.Y., SAR image despeckling: based on non-local means with non-subsample Shearlet and directional windows, Journal of Infrared and Millimeter Waves, 31, 2, pp. 159-165, (2012)
  • [10] Nezry E., Lopes A., Touzi R., Detection of structural and textural features for SAR images filtering, Proc. of the Remote Sensing: Global Monitoring for Earth Management, pp. 2169-2172, (1991)
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