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

[1] GOODMAN J W.Some fundamental properties of speckle[J].Journal Optical Society America,1976,6(11):1145-1150.
[2] LIU Z X,HU S H,XIAO Y,et al.SAR image target extraction based on 2-D leapfrog filtering [C]∥Proceedings of 2010 IEEE 0th International Conference on Signal Processing(ICSP2010).2010:943-946.
[3] 陈双叶,周耳江,吴强.ND-GSM模型的采样矩阵方向优化及SAR图像去噪[J].计算机科学,2015,2(6A):158-162,167.
[4] BUADES A,COLL B.A non-local algorithm for image denoising[J].IEEE Computer Society Conference on Computer Vision & Pattern Recognition,2005,2(7):60-65.
[5] KERVRANN C,BOULANGE R J.Optimal spatial adaptationfor patch-based image denoising[J].IEEE Transactions on Ima-ge Processing,2006,15(10):2866-2878.
[6] DABOV K,FOI A,KATKOVNIK V,et al.Image denoising by sparse 3-d transform-domain collaborative filtering[J].IEEE Transactions on Image Processing,2007,16(8):2080-2095.
[7] 范云鹏.矩阵低秩逼近在图像压缩中的应用[D].西安:西安电子科技大学,2012:26-30.
[8] 赵新斌.一类带有核范数的优化问题的梯度算法[D].北京:北京工业大学,2012:13-25.
[9] CANDES E J,Recht B.Exact matrix completion via convex optimization[J].Foundations of Computational mathematics,2012,55(6):111-119.
[10] GU S,ZHANG L,ZUO W,et al.Weighted nuclear norm minimization with application to image denoising[C]∥CVPR 2014.2014:2862-2869.
[11] DONG W S,SHI G M,LI X.Nonlocal image restoration with bilateral variance estimation:A Low-rank Approach[J].IEEE Transactions on ImageProcessing,2013,22(2):700-711.
[12] 王圳萍,张家树,陈高.加权低秩矩阵恢复的混合噪声图像去噪[J].计算机科学,2016,3(1):298-301.
[13] 黄之娟,唐超颖,陈跃庭,等.基于非局部相似性和低秩矩阵的遥感图像重构方法[J].光学学报,2016,36(6):97-107.
[14] DABOV K,FOI A,KATKOVNIK V,et al.Image restoration by sparse3D transform-domain collaborative filtering[J].Electronic Imaging,International Society for Optics and Photonics,2008,6812(8):2080-2095.
[15] NEJATI M,SAMAVI S,DERKSEN H.Denoising by low-rank and sparse representations[J].Journal of Visual Communication & Image Representation,2016,36(C):28-39.
[16] LIU X,TANAKA M,OKUTOMI M.Noise level estimationusing weak textured patches of a single noisy image[C]∥2012 19th IEEE International Conference.2012:665-668.
[17] KHAN S,JAIN A,KHARE A.Iamge denoising based on adaptive wavelet thresholding by using various shrinkage methods under different noise condition[J].International Journal of Computer Application,2012,59(20):13-17.
[18] GERARDO D M,MARIANA P,Giovanni P.BenchmarkingFramework for SAR Despeckling[J].IEEE Transactions on Geo-science and Remote Sensing,2014,52(3):1596-1615.
[19] 郝强,周敏,郑红婵.基于边缘检测和四点插值细分的图像去噪[J].计算机工程与应用,2014,50(11):184-187.

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