TD-AMRKNet-Based Radar Image Processing Framework for Sea Clutter Suppression

Sea clutter significantly impacts the radar detection of maritime targets. Existing sea clutter suppression methods often face challenges in complex dynamic marine environments, and their generalization capabilities may be limited. This paper proposes a network architecture named Triplet Diffusion Attention Multi-scale Res-KAN Net (TD-AMRKNet), based on a diffusion model and Triplet Attention. By introducing lightweight Multi-scale Generalized Spatial Convolutions (Multi-scale-GSConvs) and several small model networks, TD-AMRKNet effectively reduces model parameters, making it a compact and efficient network. The AMRK module, designed with a gating mechanism, captures long-range dependencies in images. It also integrates multi-source knowledge through cross-resolution image fusion, thereby enhancing semantic understanding and improving the representation of details and local features. TD-AMRKNet effectively suppresses sea clutter across different radar data types, including time-frequency spectrograms from staring radar and PPI images from scanning radar. Experimental results show that the model contains only 3.46M parameters and requires approximately 0.0305 seconds for Overall Average (OA) processing. It achieves competitive performance on six real-world sea clutter datasets, with clutter suppression effectiveness evaluated using PSNR, SSIM, P-S and CSR metrics.