PSDN: Physics-Guided Self-Supervised Dual-Stream Network for Ultrasound Image Denoising

Ultrasound imaging is widely used in clinical diagnosis but inherently suffers from speckle noise, which degrades image quality and obscures vital anatomical details. Existing deep learning–based denoising methods are severely constrained by the lack of clean ground-truth images, while current self-supervised approaches often neglect acoustic physical mechanisms and rely on single-stream architectures, resulting in frequency aliasing and an inherent trade-off between noise suppression and edge preservation. To address these issues, we propose a Physics-Guided Self-Supervised Dual-Stream Module (PSDN) that effectively suppresses speckle noise while preserving anatomical structures and fine details. First, we propose a Physically Guided Adaptive Frequency Decomposition (PAFD) module to alleviate the aliasing between tissue details and speckles by extracting acoustic physical priors and dynamically decomposing the noisy image into purified frequency subbands. Second, a Structure-Detail Dual-Stream (S2DS) module is proposed to balance macro-structure recovery and micro-detail modeling by processing the decomposed subbands through parallel low- and high-frequency branches. Extensive experimental results across multiple datasets demonstrate that PSDN achieves state-of-the-art denoising performance without requiring clean reference images.