合成孔径雷达图像去噪算法研究进展

doi:10.12305/j.issn.1001-506X.2026.01.10

Abstract

Abstract:

The imaging process of synthetic aperture radar （SAR） is significantly affected by speckle noise, which degrades the quality and accuracy of the images, posing challenges for subsequent image interpretation. This paper briefly summarizes the formation mechanism of SAR image speckle and compares the research progress of traditional denoising algorithms with deep learning-based denoising algorithms. Firstly, it introduces the principles and advantages/disadvantages of traditional denoising methods such as spatial filtering, non-local means, and transform domain filtering, analyzing their limitations in preserving image details and suppressing noise to clarify their application constraints. Subsequently, it summarizes the latest developments of deep learning denoising algorithms, including convolutional neural networks （CNN） and generative adversarial networks （GAN）, discussing the advantages and improvement strategies of different models. Finally, through experimental comparisons, it analyzes the performance and shortcomings of each algorithm, exploring the challenges faced by current SAR image denoising and future development trends.

Key words: synthetic aperture radar, image denoising, coherent speckle suppression, artificial intelligence, deep learning

CLC Number:

TN 957

Haiqing CHEN, Liuying WANG, Gu LIU, Long WANG, Chaoqun GE, Mengzhou CHEN. Research progress on denoising algorithms for synthetic aperture radar images[J]. Systems Engineering and Electronics, 2026, 48(1): 94-105.

Figures/Tables 10

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数据集名称	图像数量	极化方式	样本分辨率	主要目标对象	采集工具
MSTAR^[7]	超20 000	HH	128 × 128	SAR车辆	聚束式SAR
OpenSARShip^[8]	11 346	VV、VH	100 × 100	SAR船舶	Sentinel-1卫星
FuSAR-Ship^[9]	16 144	DH、DV	512 × 512	SAR船舶	高分三号卫星
OpenSARUrban^[10]	33 358	VV、VH	100 × 100	SAR城市	Sentinel-1卫星
SAR-ACD^[11]	4 322	HH	未公开	SAR飞机	高分三号卫星

算法类型	优点	缺点
经典空域滤波	简单易实现；可用于抑制噪声，改善图像质量；特定滤波器（如均值滤波）对高斯噪声具有较好效果	对复杂噪声和低信噪比图像的处理效果有限；性能受到滤波器窗口大小的影响较大，可能导致细节模糊化，对目标信号边缘保护性能相对较差
基于小波变换	能够较好保留图像的细节特征；对高斯噪声和脉冲噪声具有较好效果	对复杂噪声和非线性噪声处理能力有限；边缘保护能力相对较差，可能引入新的伪影
基于Contourlet变换	能更好地捕捉图像纹理和结构信息；对复杂噪声类型有较好效果	对计算资源要求较高；处理过程中可能引入新的伪影，不符合MRA理论
基于Shearlet变换	能够更好地处理图像的纹理和微观结构信息；对相干斑噪声和高斯噪声有较好效果	计算复杂度较高；处理过程中可能引入新的伪影
基于非局部均值	能够保留图像细节；对脉冲噪声和斑点噪声具有较好效果	计算复杂度较高；需要提前估计噪声水平；在处理低信噪比图像时效果有限

Method	PPB	SAR-BM3D	SAR-DRN	SAR-RDCP	MONet	MRDDANet	NB2NB	B2UB	SARDeseg
PSNR	17.38	17.94	21.35	21.3	21.22	21.51	21.2	21.32	21.41
ENL	310.2648	208.6316	69.8153	74.1399	61.0177	110.8038	104.1671	36.1941	167.714 0
FOM	0.9158	0.9454	0.9361	0.9445	0.9357	0.9454	0.9507	0.9436	0.9555
RGPI	−0.2432	0.0264	0.0842	0.0977	0.0925	0.0927	0.0571	0.1083	0.1049

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Research progress on denoising algorithms for synthetic aperture radar images

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