D2TR: Sea Clutter Suppression via Dynamic Dual-Tree Complex Wavelet Selection and Target-Guided Regularization

Marine radar imaging systems frequently encounter performance degradation caused by sea clutter interference in complex oceanic environments. While numerous sea clutter suppression methods have been proposed, their generalization capabilities remain substantially constrained by two critical factors: inadequate hierarchical feature extraction of both clutter patterns and target signatures, and insufficient utilization of domain-specific characteristics. Current approaches fail to comprehensively exploit the distinctive high-frequency sub-band manifestations of clutter in the frequency domain, while simultaneously neglecting the sparse distribution of targets in the spatial domain. To address these limitations, we propose a novel sea clutter suppression algorithm. First, a dynamic dual-tree complex wavelet selection mechanism is introduced to enhance frequency-domain feature extraction, effectively capturing clutter features across high-frequency subbands and achieving better separation of clutter and targets. Second, leveraging the sparse distribution of targets in PPI image, a target-guided regularization is designed to strengthen spatial-domain target feature extraction, suppress background interference and preserve target information. Finally, a comprehensive large-scale sea clutter suppression dataset, RadarP-PIV2, is developed, encompassing diverse and complex scenarios to enable systematic qualitative and quantitative evaluations. Experimental results demonstrate that the proposed algorithm significantly outperforms existing methods in sea clutter suppression tasks, showcasing superior practical value and robust generalization capabilities.