single-jc.php

JACIII Vol.23 No.1 pp. 107-113
doi: 10.20965/jaciii.2019.p0107
(2019)

Paper:

SAR Image Denoising Algorithm Based on Bayes Wavelet Shrinkage and Fast Guided Filter

Xiu Jie Yang* and Ping Chen**

*Computer College, Chongqing College of Electronic Engineering
No.48 Middle Road, University Town, Shapingba District, Chongqing 401331, China

**Training and Continuing Education College, Chongqing College of Electronic Engineering
No.48 Middle Road, University Town, Shapingba District, Chongqing 401331, China

Received:
May 8, 2018
Accepted:
July 2, 2018
Published:
January 20, 2019
Keywords:
SAR image, wavelet, fast guided filter, Bayes maximum a posteriori estimation
Abstract

To remove the speckle noise of synthetic aperture radar (SAR) images, a novel denoising algorithm based on Bayes wavelet shrinkage and a fast guided filter is proposed. According to the statistical properties of SAR images, the noise-free signal and speckle noise in the wavelet domain are modeled as Laplace and Fisher-Tippett distributions respectively. Then a new wavelet shrinkage algorithm is obtained by adopting the Bayes maximum a posteriori estimation. Speckle noise in the high-frequency domain of SAR images is shrunk by this new wavelet shrinkage algorithm. As the wavelet coefficients of the low-frequency domain also contain some speckle noise, speckle noise in the low-frequency domain can be further filtered by the fast guided filter. The result of the denoising experiments of simulated SAR images and real SAR images demonstrate that the proposed algorithm has the ability to better denoise and preserve edge information.

Cite this article as:
X. Yang and P. Chen, “SAR Image Denoising Algorithm Based on Bayes Wavelet Shrinkage and Fast Guided Filter,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.1, pp. 107-113, 2019.
Data files:
References
  1. [1] W. Ni and X. Gao, “Despeckling of SAR Image Using Generalized Guided Filter With Bayesian Nonlocal Means,” IEEE Trans. on Geoscience & Remote Sensing, Vol.54, No.1, pp. 567-579, 2015.
  2. [2] J. Zhao, P. P. Wang, and G. Z. Men, “SAR Image Denosing Based on Nonlocal Similarity and Low Rank Matrix Approximation,” Computer Science, 2017.
  3. [3] M. Zhang, M. Zheng, and K. Liao, “SAR Image De-noising Method Based on Edge Direction Features of Haar Wavelet Domain,” Computer Applications & Software, 2017.
  4. [4] A. S. Yommy, R. Liu, S. O. Onuh, et al., “SAR image despeckling and compression using K-nearest neighbour based lee filter and wavelet,” Int. Congress on Image and Signal Processing, IEEE, pp. 158-167, 2016.
  5. [5] A. Pandit, M. Sharma, and R. Ramsankaran, “Comparison of the performance of the newly developed CDWM Filter with Enhanced LEE and Enhanced Frost Filters over the SAR image,” Int. Conf. on Industrial and Information Systems, IEEE, pp. 1-5, 2015.
  6. [6] H. Choi and J. Jeong, “Despeckling Images Using a Preprocessing Filter and Discrete Wavelet Transform-Based Noise Reduction Techniques,” IEEE Sensors J., Vol.18, No.8, pp. 3131-3139, 2018.
  7. [7] V. Bhateja, A. Tripathi, A. Gupta, et al., “Speckle suppression in SAR images employing modified anisotropic diffusion filtering in wavelet domain for environment monitoring,” Measurement, Vol.74, pp. 246-254, 2015.
  8. [8] M. Mastriani, “New wavelet-based superresolution algorithm for speckle reduction in SAR images,” arXiv:1608.00270, 2016.
  9. [9] G. D. Martino, A. D. Simone, A. Iodice, et al., “Scattering-Based Nonlocal Means SAR Despeckling,” IEEE Trans. on Geoscience & Remote Sensing, Vol.54, No.6, pp. 3574-3588, 2016.
  10. [10] W. Wang, X. Liu, and W. A. Zhang, “Downsampled SAR-BM3D Despeckling Approach for Single-Look SAR Images in High Resolution,” J. of Computer & Communications, Vol.4, No.15, pp. 126-131, 2016.
  11. [11] A. Achim, E. Kuruolu, and J. Zerubia, “SAR image filtering based on the heavy-tailed Rayleigh model,” IEEE Trans. on Image Processing, Vol.15, No.9, pp. 2686-2693, 2006.
  12. [12] J. Wang, Y. Liu, W. He, and Z. Chao, “Blind Separation Anti-jamming Method for Single-channel Using Wavelet Decomposition,” J. of Chongqing University of Posts and Telecommunications (Natural Science Edition), Vol.26, No.5, pp. 648-653, 2014.
  13. [13] D. L. Donoho and I. M. Johnstone, “Ideal Spatial Adaptation via Wavelet Shrinkage,” Biometrika, Vol.81, pp. 425-455, 1994.
  14. [14] K. He, J. Sun, and X. Tang, “Guided image filtering,” In the 11th European Conf. on Computer Vision (ECCV), pp. 1-14. 2010.
  15. [15] K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), Vol.35, No.6, pp. 1397-1409, 2013.
  16. [16] K. He and J. Sun, “Fast Guided Filter,” Computer Science, 2015.
  17. [17] S. J. Xie, Y. Lu, S. Yoon, et al., “Intensity variation normalization for finger vein recognition using guided filter based singe scale retinex,” Sensors, Vol.15, No.7, pp. 17089-17105, 2015.
  18. [18] T. Helin and M. Burger, “Maximum a posteriori probability estimates in infinite-dimensional Bayesian inverse problems,” Inverse Problems, Vol.31, No.8, 085009, 2015.
  19. [19] S. Bandyopadhyay and S. J. Chung, “Distributed estimation using bayesian consensus filtering,” American Control Conf. (ACC), IEEE, pp. 634-641, 2014.
  20. [20] N. Gupta, M. N. S. Swamy, and E. Plotkin, “Despeckling of Medical Ultrasound Images Using Data and Rate Adaptive Lossy Compression,” IEEE Trans. on Medical Imaging, Vol.24, No.6, pp. 743-754, 2005.
  21. [21] J. Glaister, A. Wong, and D. A. Clausi, “Despeckling of Synthetic Aperture Radar Images Using Monte Carlo Texture Likelihood Sampling,” IEEE Trans. on Geoscience & Remote Sensing, Vol.52, No.2, pp. 1238-1248, 2014.
  22. [22] A. Fathi and A. R. Naghsh-Nilchi, “Efficient image denoising method based on a new adaptive wavelet packet thresholding function,” IEEE Trans. on Image Processing, Vol.21, No.9, pp. 3981-3990, 2012.
  23. [23] Rajesh Mohan R., S Mridula, and P. Mohanan, “Speckle noise reduction in images using Wiener filtering and adaptive Wavelet thresholding,” 2016 IEEE Region 10 Conf. (TENCON), pp. 2860-2863, 2016.
  24. [24] Y. Tong, Q. Zhang, and Y. Qi, “Image Quality Assessing by Combining PSNR with SSIM,” J. of Image and Graphics, Vol.11, No.12, pp. 19-24, 2016.
  25. [25] A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a ‘Completely Blind’ Image Quality Analyzer,” IEEE Signal Process, Vol.20, No.3, pp. 209-212, 2013.
  26. [26] J. Zhang, C. Wang, and Y. Cheng, “Comparison of despeckle filters for breast ultrasound images,” Circuits, Systems, and Signal Processing, Vol.34, No.1, pp. 185-208, 2015.
  27. [27] S. Gupta, R. C. Chauhan, and S. C. Sexana, “Locally adaptive wavelet domain Bayesian processor for denoising medical ultrasound images using Speckle modelling based on Rayleigh distribution,” IEEE Proc. Vis. Image Signal Process., Vol.152, No.1, pp. 129-135, 2005.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Oct. 11, 2024