Radardiff: Improving Sea Clutter Suppression Using Diffusion Models for Radar Images

Marine radar is employed across multiple fields, notably in navigation, meteorology, defense, and security. Marine radar images are highly sensitive to sea clutter, highlighting the crucial importance of sea clutter suppression in radar image processing. However, existing algorithms for sea clutter suppression often struggle to effectively generalize in complex marine environments. In this paper, we introduce RadarDiff, a novel approach that leverages diffusion models to enhance sea clutter suppression in marine radar plan-position indicator (PPI) images. We treat sea clutter suppression as an image-to-image translation task and propose a novel data augmentation method to create image pairs with and without sea clutter. Additionally, we introduce a unique loss function designed to address the challenge of small targets disappearing after suppression. To our knowledge, we are the first to utilize the diffusion-based model in sea clutter suppression for radar PPI images. Our quantitative and qualitative results demonstrate significant improvements compared to traditional denoising methods and classical GAN-based models.