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


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

May 8, 2018
July 2, 2018
January 20, 2019
SAR image, wavelet, fast guided filter, Bayes maximum a posteriori estimation

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.
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Last updated on Feb. 20, 2019