Ultrasound denoising is essential for mitigating speckle-induced degradations, thereby enhancing image quality and improving diagnostic reliability. Nevertheless, because speckle patterns inherently encode both texture and fine anatomical details, effectively suppressing noise while preserving structural fidelity remains a significant challenge. In this study, we propose a prior-guided hierarchical instance-pixel contrastive learning model for ultrasound denoising, designed to promote noise-invariant and structure-aware feature representations by maximizing the separability between noisy and clean samples at both pixel and instance levels. Specifically, a statistics-guided pixel-level contrastive learning strategy is introduced to enhance distributional discrepancies between noisy and clean pixels, thereby improving local structural consistency. Concurrently, a memory bank is employed to facilitate instance-level contrastive learning in the feature space, encouraging representations that more faithfully approximate the underlying data distribution. Furthermore, a hybrid Transformer-CNN architecture is adopted, coupling a Transformer-based encoder for global context modeling with a CNN-based decoder optimized for fine-grained anatomical structure restoration, thus enabling complementary exploitation of long-range dependencies and local texture details. Extensive evaluations on two publicly available ultrasound datasets demonstrate that the proposed model consistently outperforms existing methods, confirming its effectiveness and superiority.

翻译：超声去噪对于减轻斑点噪声引起的图像退化至关重要，从而提升图像质量并提高诊断可靠性。然而，由于斑点模式本身同时编码了纹理和精细的解剖细节，在有效抑制噪声的同时保持结构保真度仍然是一个重大挑战。在本研究中，我们提出了一种用于超声去噪的先验引导分层实例-像素对比学习模型，该模型旨在通过在像素级和实例级最大化噪声样本与干净样本之间的可分离性，从而促进噪声不变且结构感知的特征表示。具体而言，我们引入了一种统计引导的像素级对比学习策略，以增强噪声像素与干净像素之间的分布差异，从而提高局部结构一致性。同时，采用一个记忆库来促进特征空间中的实例级对比学习，鼓励特征表示更忠实地逼近底层数据分布。此外，模型采用了混合Transformer-CNN架构，将基于Transformer的编码器（用于全局上下文建模）与基于CNN的解码器（针对细粒度解剖结构恢复进行优化）相结合，从而实现对长程依赖关系和局部纹理细节的互补性利用。在两个公开可用的超声数据集上进行的大量评估表明，所提出的模型始终优于现有方法，证实了其有效性和优越性。