Journal of Systems Engineering and Electronics ›› 2012, Vol. 34 ›› Issue (12): 2451-2455.doi: 10.3969/j.issn.1001-506X.2012.12.08
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CAI Yu-chen, ZHAO Bao-jun, TANG Lin-bo
Online:
Published:
Abstract:
Recently, a non-local means (NL-means) algorithm is proposed for the denoising of Gaussian noises, and it can effectively preserve textures while removing noises. However, the computation of NL-means is extremely heavy, and furthermore, the Gaussianity of the noise is required. Due to these limitations, NL-means is not suitable for synthetic aperture radar (SAR) image despeckling. A similar points matching method based upon gradient grouping is presented, and this method leads to an improved NL-means model. Compared with the existing NL-means, the improved model can achieve a better despeckling performance with lower computation. On the other hand, since SAR image is with multiplicative noise, the holomorphic transform is introduced as a pre-processing to cater for SAR image despcekling. Experimental results demonstrate that the proposed method achieves 3 dB of PSNR gain in comparison with the existing relevant methods and the computation is around 3 times faster than that of the NL-means.
CLC Number:
TN 957.51
CAI Yu-chen, ZHAO Bao-jun, TANG Lin-bo. Improved NL-means algorithm based on gradient grouping for SAR image despeckling[J]. Journal of Systems Engineering and Electronics, 2012, 34(12): 2451-2455.
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URL: https://www.sys-ele.com/EN/10.3969/j.issn.1001-506X.2012.12.08
https://www.sys-ele.com/EN/Y2012/V34/I12/2451