Computer Science ›› 2017, Vol. 44 ›› Issue (Z6): 183-187.doi: 10.11896/j.issn.1002-137X.2017.6A.042

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SAR Image Denosing Based on Nonlocal Similarity and Low Rank Matrix Approximation

ZHAO Jie, WANG Pei-pei and MEN Guo-zun   

  • Online:2017-12-01 Published:2018-12-01

Abstract: The SAR image denoising based on nonlocal similarity and low rank matrix approximation was presented to minimize the effect of speckle noise in Synthetic aperture radar.Firstly,multiplicative speckle is changed into additive noise by logarithmic transformation.Secondly,the image’s global noise variance is estimated in advance.Thirdly,a new joint block matching method based on Euclidean distance and R-squared is developed,which makes the matching result more accurate.Finally,within the framework of the low rank model,the improved residual noise variance estimation is used to approximate the low rank matrix with the weighted nuclear norm minimization.The noise suppression of SAR image is achieved.The experimental results show that this method not only the peak signal to noise ratio objective indicators have significantly improved and preserved the local structure of the image better,and produces a good subjective visual effect.

Key words: SAR image denosing,Joint block matching,Non-local selfsimilarity,Weighted nuclear norm minimization

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