基于稀疏重构框架下剪切波的SAR图像去噪

纪建; 李晓; 许双星; 刘欢; 黄静静

doi:10.16383/j.aas.2015.e130004

基于稀疏重构框架下剪切波的SAR图像去噪

doi: 10.16383/j.aas.2015.e130004

纪建^1,,
李晓^1,2,
许双星^1,2,
刘欢¹,
黄静静¹

1.
西安电子科技大学西安710071;
2.
华为科技有限公司西安710071

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出版历程
- 收稿日期: 2013-01-16
- 修回日期: 2015-03-31
- 刊出日期: 2015-08-20

SAR Image Despeckling by Sparse Reconstruction Based on Shearlets

1.
School of Computer Science and Technology, Xidian University, Xi'an 710071, China;
2.
Huawei Technology Co. Ltd, Xi'an 710071, China

Funds:

Supported by National Natural Science Foundation of China (61101248), the Fundamental Research Fund for the Central Universities of China (JB140315)

More Information

Corresponding author: JI Jian Received the B.S.degree in computational mathematics from the Northwest University,China in 1993,and the Ph.D.degree in computer science and technology from the Northwestern Polytechnical University,China,in 2007.She is an associate professor at School of Computer Science and Technology,Xidian University,China.Her research interest covers computational intelligence,pattern recognition,and image analysis,both in theory and applications.Corresponding author of this paper. E-mail:jji@xidian.edu.cn

摘要

摘要: SAR图像很容易被乘性噪声多污染，进而影响SAR图像后序的分析与处理。本文中提出了一种基于剪切波稀疏编码的SAR图像移除乘性噪声的新模型。首先通过压缩感知理论建立SAR图像去噪模型；其次通过剪切波变换获得剪切波系数，每个尺度的系数视为一个单元；对于每个单元，通过剪切波域的贝叶斯估计对稀疏系数进行迭代估计。重现的单元最后结合起来构造去噪后的图像。SAR图像去噪效果显示了该算法有良好的表现性，对噪声具有鲁棒性；本文提出的算法不仅有较好的去噪效果，而且还保存了更多的边界信息。
- 合成孔径雷达 /
- 压缩感知 /
- 稀疏重构 /
- 剪切波
Abstract: Synthetic aperture radar (SAR) image is usually polluted by multiplicative speckle noise, which can affect further processing of SAR image. This paper presents a new approach for multiplicative noise removal in SAR images based on sparse coding by shearlets filtering. First, a SAR despeckling model is built by the theory of compressed sensing (CS). Secondly, obtain shearlets coefficient through shearlet transform, each scale coefficient is represented as a unit. For each unit, sparse coefficient is iteratively estimated by using Bayesian estimation based on shearlets domain. The represented units are finally collaboratively aggregated to construct the despeckling image. Our results in SAR image despeckling show the good performance of this algorithm, and prove that the algorithm proposed is robustness to noise, which is not only good for reducing speckle, but also has an advantage in holding information of the edge.
- Synthetic aperture radar (SAR) /
- compressed sensing (CS) /
- sparse reconstruction /
- shearlets

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参考文献(13)

[1]	Lee J S. Digital image enhancement and noise filtering by use of local statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1980, PAMI-2(2): 165-168
[2]	[2] Kuan D T, Sawchuk A A, Strand T C, Chavel P. Adaptive restoration of images with speckle. IEEE Transactions on Acoustics, Speech and Signal Process, 1987, 35(3): 373-383
[3]	[3] Frost V S, Stiles J A, Shanmugan K S, Holtzman J C. A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982, PAMI-4(2): 157-166
[4]	[4] Easley G, Labate D, Lim W Q. Sparse directional image representations using the discrete shearlet transform. Applied and Computational Harmonic Analysis, 2008, 25(1): 25-46
[5]	[5] Jiao X, Wen X B. SAR image segmentation based on Markov random field model and multiscale technology. In: Proceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery, 2009. Tianjin, China: IEEE, 2009. 442-446
[6]	[6] Hou B, Guan H, Jiang J G, Liu K, Jiao L C. SAR image despeckling based on improved directionlet domain Gaussian mixture model. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Vancouver, BC: IEEE, 2011. 3795-3798
[7]	[7] Baraniuk R G. Compressive sensing. IEEE Signal Processing Magazine, 2007, 24(4): 118-121
[8]	[8] Daubechies I, Defrise M, Mol C D. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Communications on Pure and Applied Mathematics, 2004, 57(11): 1413-1457
[9]	[9] Cands E, Demanet L, Donoho D, Ying L X. Fast discrete curvelet transforms. SIAM Journal on Multiscale Modeling and Simulation, 2005, 5(3): 861-899
[10]	Do M N, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation. IEEE Transaction on Image Processing, 2005, 14(12): 2091-2106
[11]	Wright S J, Nowak R D, Figueiredo M A T. Sparse reconstruction by separable approximation. IEEE Transactions on Signal Processing, 2009, 57(7): 2479-2493
[12]	Achim A, Tsakalides P, Bezerianos A. SAR image denoising via Bayesian wavelet shrinkage based on heavy tailed modelling. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(8): 1773-1784
[13]	Baraldi A, Parmigiani F. A refined Gamma MAP SAR speckle filter with improved geometrical adaptivity. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(5): 1245-1257

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