MSPaDet: A Multi-Scale Phase-Aware Denoising Method for Target Detection in SAR Images

Synthetic Aperture Radar (SAR) target detection remains challenging due to coherent speckle corruption, weak-scattering targets with degraded structural cues, and cross-scale inconsistencies under anisotropic scattering. To tackle these challenges, this paper presents MSPaDet, a novel multi-scale phase-aware denoising detection framework that advances SAR target detection by deeply integrating phase coherence with multi-scale representation learning. The proposed method introduces explicit dual-tree complex wavelet transform decomposition to generate direction-selective complex sub-bands, enabling fine-grained sub-band modulation. Within the framework, an SCFRDeno module suppresses speckle-dominant responses while preserving high-frequency structures via phase-coherence-guided reweighting, and a PaSCA block further refines features through input-adaptive spatial focusing and region reweighting. Extensive experiments on public SAR detection benchmarks—including MSAR, SAR-Aircraft-1.0, and SARDet-100K—demonstrate that our approach consistently outperforms state-of-the-art methods in detection accuracy, robustness, and cross-scenario generalization, with moderate computational cost, showing promising potential for practical deployment in Earth observation and safety monitoring systems.