SAR image denoising via fast weighted nuclear norm minimization

被引：2
|
作者：
Wang C. [1 ]
Zhao H. [1 ]
Wang J. [2 ]
Li X. [2 ]
Huang P. [1 ]
机构：
[1] College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing
[2] Beijing Institute of Electronic System Engineering, Beijing
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2019年 / 41卷 / 07期
关键词：
Image denoising; Nuclear norm; Singular value decomposition; Synthetic aperture radar (SAR);
D O I：
10.3969/j.issn.1001-506X.2019.07.10
中图分类号：
学科分类号：
摘要：
A synthetic aperture radar (SAR) image denoising method based on fast weighted nuclear norm minimization is proposed. Firstly, multiplicative speckle noise is converted to additive noise by logarithmic transformation. Secondly, the nonlocal similarity is used for image block matching. Next, according to the framework of the low rank model, random singular value decomposition is introduced to replace the singular value decomposition in the weighted nuclear norm minimization (WNNM) algorithm for approximating the low-rank matrix. Then to enhance the texture of the image, the gradient histogram preservation method is used. Finally, fast denoising of SAR images is achieved. Experiments on the MSTAR database show that the proposed approach is effective in SAR image denosing and the edge preserving in comparison with some traditional algorithms. Moreover, it is three times faster than the WNNM method. © 2019, Editorial Office of Systems Engineering and Electronics. All right reserved.
引用
收藏
页码：1504 / 1508
页数：4
相关论文
共 30 条
  • [1] Wang C.Y., Hu Y.K., Wu S.X., Shearlet domain SAR image denoising method based on Bayesian model, Systems Engineering and Electronics, 39, 6, pp. 1250-1255, (2017)
  • [2] Nafornita C., Nelson J.D.B., ISAR A.Performance analysis of SAR image denoising using scaling exponent estimator, Proc.of the IEEE International Conference on Communications, (2016)
  • [3] Yi Z.L., Yin D., Hu A.Z., Et al., SAR image des-peckling based on non-local means filter, Journal of Electronics and Information Technology, 34, 4, pp. 950-955, (2012)
  • [4] Lee J.S., Refined filtering of image noise using local statistics, Computer Graphics & Image Processing, 15, 4, pp. 380-389, (1981)
  • [5] Pandit A., Sharma M., Ramsankaran R., Comparison of the performance of the newly developed CDWM filter with enhanced LEE and enhanced frost filters over the SAR image, Proc.of the IEEE International Conference on Industrial and Information Systems, pp. 1-5, (2015)
  • [6] Ji J., Li Y., An improved SAR image denoising method based on bootstrap statistical estimation with ICA basis, Chinese Journal of Electronics, 25, 4, pp. 786-792, (2016)
  • [7] Ray A., Kartikeyan B., Garg S., Towards deriving an optimal approach for denoising of RISAT-1 SAR data using wavelet transform, International Journal of Computerences & Engineering, 4, 10, pp. 33-46, (2016)
  • [8] Anbouhi M.K., Ghofrani S., Weighted Bayesian based speckle de-noising of SAR image in contourlet domain, Proc.of the IEEE Electrical Engineering, pp. 251-254, (2015)
  • [9] Gleich D., Datcu M., Wavelet-based des-peckling of SAR images using Gauss-Markov random fields, IEEE Trans.on Geoscience & Remote Sensing, 45, 12, pp. 4127-4143, (2007)
  • [10] Molina D.E., Gleich D., Datcu M., Gibbs random field models for model-based des-peckling of SAR images, IEEE Geoscience & Remote Sensing Letters, 7, 1, pp. 73-77, (2010)
123