SAFUS-Net: self-supervised attention-guided framework for ultrasound speckle suppression

Abstract

Abstract In medical image analysis, diagnostic interpretation and downstream clinical tasks can be strongly influenced by data quality, particularly in ultrasound imaging, where speckle noise and low contrast can obscure critical anatomical details. To address these challenges, we propose SAFUS-Net, a self-supervised, attention-guided framework for ultrasound speckle suppression that learns directly from noisy data without requiring clean reference images. Unlike conventional supervised approaches, which depend on difficult-to-acquire clean targets, training pairs are generated using a multiplicative noise simulation strategy, enabling reproducible and scalable speckle-to-speckle learning. An auxiliary constant input channel is introduced to stabilize optimization by providing an input-dependent bias across receptive fields, facilitating smoother gradient propagation and improved convergence. In addition, an Adaptive Frequency-Channel Attention module is incorporated at the model bottleneck to enhance feature representation by emphasizing salient anatomical structures while suppressing irrelevant background responses. Extensive experiments are performed on both synthetic and clinical ultrasound datasets spanning multiple anatomical regions and imaging characteristics. The results demonstrate that SAFUS-Net consistently outperforms existing enhancement methods across both reference-based and no-reference evaluation metrics, achieving a more favorable balance between noise reduction, contrast enhancement, and edge preservation. These findings indicate that SAFUS-Net is a robust and practical approach for ultrasound image enhancement, with potential value for improving clinical image interpretation and supporting downstream ultrasound analysis tasks.