发表机构
Technical University of Munich; Munich Center for Machine Learning; The University of Hong Kong（慕尼黑工业大学; 慕尼黑机器学习中心; 香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对超声斑点噪声的空间相关性，提出基于块状掩蔽修复的自监督去斑框架Mask2Restore，并引入跨分辨率正则化，在模拟和体内数据上改善斑点-细节权衡并保留解剖结构。
AI 中文摘要
医学超声（US）图像固有地受到斑点噪声的退化影响，斑点是一种颗粒状干涉图案，在图像恢复中常被视为一种复杂形式的噪声。然而，与随机噪声不同，US斑点源于组织内的相干散射，因此在固定采集条件下具有高度空间依赖性和确定性，这使得US斑点抑制从根本上不同于自然图像去噪。由于无斑点的US目标在实际中不可获得，自监督去噪是必要的。盲点网络（BSN）是自然图像中占主导地位的自监督范式，但其逐像素掩蔽策略假设噪声在空间上独立，这一假设与US斑点不匹配，因为US斑点在多个像素上空间相关，而非逐像素独立。为解决这一不匹配问题，我们提出了Mask2Restore，一种自监督US去斑框架，将去斑重新表述为在单个噪声图像上进行块状掩蔽的上下文修复。与逐像素BSN掩蔽不同，块状掩蔽通过移除局部相关的斑点邻域，并将网络使用的重建线索从相邻斑点相关性转移到更广泛的解剖上下文，从而解决多像素斑点相关性。我们进一步引入了跨分辨率上下文正则化（CRCR），通过强制多分辨率预测之间的一致性来抑制残余斑点偏差。在模拟和体内颈动脉US、未见过的精细结构案例以及下游心脏分割上的实验表明，该方法改善了斑点-细节权衡，更好地保留了精细解剖结构，并对后续图像分析具有实用价值。
英文摘要
Medical ultrasound (US) is inherently degraded by speckle, a granular interference pattern that is often treated as a complex form of noise in image restoration. However, unlike random noise, US speckle originates from coherent scattering within tissue and is therefore highly spatially dependent and deterministic under fixed acquisition conditions, making US speckle suppression fundamentally different from natural image denoising. Because speckle-free US targets are unavailable in practice, self-supervised denoising is necessary. Blind-spot networks (BSN) are the dominant self-supervised paradigm for natural images, but their pixel-wise masking strategy assumes spatially independent noise, an assumption poorly matched to US speckle, which is spatially correlated over multiple pixels rather than pixel-wise independent. To address this mismatch, we propose Mask2Restore, a self-supervised US despeckling framework that reformulates despeckling as contextual inpainting with block-wise masking on single noisy images. Unlike pixel-wise BSN masking, block-wise masking addresses this multi-pixel speckle correlation by removing locally correlated speckle neighborhoods and shifting the reconstruction cues used by the network from adjacent speckle correlations to broader anatomical context. We further introduce cross-resolution context regularization (CRCR), which suppresses residual speckle bias by enforcing consistency across multi-resolution predictions. Experiments on simulated and in vivo carotid US, unseen fine-structure cases, and downstream cardiac segmentation demonstrate improved speckle-detail trade-offs, better preservation of fine anatomical structures, and practical value for subsequent image analysis.
