摘要
为进一步提升深度学习方法对合成孔径雷达(SAR)图像相干斑的抑制与边缘保持性能,提出了一种边缘引导的双分支网络相干斑抑制方法。构建了一种由边缘信息提取模块与双分支抑斑网络2部分构成的新型抑斑网络模型。采用密集级联方式构建边缘信息提取模块,增强模型的边缘感知能力;利用基于通道注意力的残差抑斑子网络(CARNet)、基于混合注意力的增强抑斑子网络(MAENet)及基于多分支并行的多尺度特征融合模块(MPMFFB)共同形成双分支抑斑网络,实现在相干斑抑制的同时更好地保护边缘细节。实验结果表明:与SAR-Transformer、HTNet等先进方法相比,所提方法具有更好的相干斑抑制与边缘保持性能;对仿真SAR图像,峰值信噪比、结构相似性、边缘保持指数分别平均提升0.96 dB、2.60%、0.60%;对真实SAR图像,等效视数提升14.12%以上,边缘保持指数平均提升4.52%。
In order to further improve the coherence speckle suppression and edge preservation performance of synthetic aperture radar(SAR)images by deep learning methods,a coherence speckle suppression method based on edge-guided dual-branch network was proposed.The method constructed a new speckle suppression network model,which consisted of an edge information extraction block and a dual-branch speckle suppression network.Firstly,a dense cascade strategy was used to build the edge information extraction block to enhance the edge perception capability of the model.Secondly,the channel attention-based residual despeckling network(CARNet),the mixed attention-based enhanced despeckling network(MAENet),and the multi-branch parallel based multi-scale feature fusion block(MPMFFB)were used to form a dual-branch speckle suppression network,so as to better preserve edge details while suppressing coherence speckles.The experimental results show that the proposed method has better coherence speckle suppression and edge preservation performance compared with recent state-of-the-art methods such as SAR-Transformer and HTNet.For the simulated SAR images,the peak signal to noise ratio,structural similarity index measure,and edge preserve index are improved by 0.96 dB,2.60%,and 0.60%on average,respectively.For the real SAR images,the equivalent number of looks is improved by more than 14.12%,and the edge preserve index is improved by 4.52%on average.
作者
朱磊
姚同钰
车晨洁
姚丽娜
张博
潘杨
ZHU Lei;YAO Tongyu;CHE Chenjie;YAO Lina;ZHANG Bo;PAN Yang(School of Electronics and Information,Xi’an Polytechnic University,Xi’an 710048,China)
出处
《北京航空航天大学学报》
北大核心
2025年第6期1852-1862,共11页
Journal of Beijing University of Aeronautics and Astronautics
基金
国家自然科学基金(61971339)
陕西省重点研发计划(2019GY-113)
陕西省自然科学基础研究计划(2019JQ-361)。
关键词
图像去噪
合成孔径雷达图像
相干斑抑制
双分支网络
多尺度特征融合
image denoising
synthetic aperture radar image
coherence speckle suppression
dual-branch network
multi-scale feature fusion
