MTCSAR: Fusing Multitemporal Interaction With Coherence Prior for SAR Image Denoising
Jianwei Liu, Xin Bing Su, Yi Xiao, Jie Li, Qiangqiang Yuan · IEEE Transactions on Geoscience and Remote Sensing · 2024
Synthetic aperture radar (SAR) images are inherently affected by speckle noise due to their imaging principles, significantly impacting downstream research. Denoising methods based on deep learning have garnered attention and are gradually maturing, yet they face certain challenges. Denoising single SAR images lacks temporal information, often resulting in structural fitting that introduces artifacts. The existing denoising techniques do not adequately account for the differences between SAR and optical images, despite their distinct structural characteristics. In addition, mainstream deep learning approaches heavily rely on data-driven methods, with limited consideration for statistical properties. In response to these challenges, we propose a novel network for denoising SAR images fusing multitemporal interaction with coherence prior, termed MTCSAR. The proposed network leverages multitemporal interaction (MTI) to gather information from different moments in various regions. The redundancy across these temporal dimensions helps mitigate artifacts and edge blurring. To address SAR image characteristics, we design a dual-branch network combining global and local information to handle large-scale regions and fine structures. In addition, we incorporate prior coherence information from SAR images into the network, utilizing statistical properties to enhance transparency during training. Experimental results on simulated and real datasets demonstrate that injecting MTIs and coherence improves our method’s qualitative and quantitative performance, surpassing current state-of-the-art algorithms. This validates the effectiveness of the proposed MTCSAR for multitemporal SAR image denoising.