Hybrid Transformer-CNN with Two-Stage Training for SAR Despeckling

Coherent speckle noise in Synthetic Aperture Radar (SAR) imagery severely degrades image fidelity, compromising the accuracy and reliability of subsequent advanced interpretation tasks such as target recognition, scene classification, and semantic segmentation. Convolutional neural networks (CNNs) face a significant domain gap due to their reliance on synthetic data, resulting in insufficient generalization capabilities in real-world scenarios. To address this challenge, we propose a novel denoising framework centered on a hybrid network combining hierarchical Transformers and CNNs. This framework bridges the domain gap through a two-stage 'pre-training-fine-tuning' paradigm and achieves end-to-end optimization via a spatiotemporalfrequency domain joint loss function. Experiments on synthetic and real SAR datasets demonstrate that our approach achieves superior performance across multiple key metrics and visual quality, significantly enhancing despeckling capabilities and detail fidelity.