Image denoising is a fundamental task in computer vision, particularly in medical ultrasound (US) imaging, where speckle noise significantly degrades image quality. Although recent advancements in deep neural networks have led to substantial improvements in denoising for natural images, these methods cannot be directly applied to US speckle noise, as it is not purely random. Instead, US speckle arises from complex wave interference within the body microstructure, making it tissue-dependent. This dependency means that obtaining two independent noisy observations of the same scene, as required by pioneering Noise2Noise, is not feasible. Additionally, blind-spot networks also cannot handle US speckle noise due to its high spatial dependency. To address this challenge, we introduce Speckle2Self, a novel self-supervised algorithm for speckle reduction using only single noisy observations. The key insight is that applying a multi-scale perturbation (MSP) operation introduces tissue-dependent variations in the speckle pattern across different scales, while preserving the shared anatomical structure. This enables effective speckle suppression by modeling the clean image as a low-rank signal and isolating the sparse noise component. To demonstrate its effectiveness, Speckle2Self is comprehensively compared with conventional filter-based denoising algorithms and SOTA learning-based methods, using both realistic simulated US images and human carotid US images. Additionally, data from multiple US machines are employed to evaluate model generalization and adaptability to images from unseen domains. \textit{Code and datasets will be released upon acceptance.

翻译：图像去噪是计算机视觉领域的一项基础任务，在医学超声成像中尤为重要，因为散斑噪声会显著降低图像质量。尽管深度神经网络的最新进展已使自然图像去噪性能大幅提升，但这些方法无法直接应用于超声散斑噪声，因为该噪声并非完全随机。超声散斑源于人体微观结构内复杂的波干涉现象，具有组织依赖性。这种依赖性意味着无法获得同一场景的两个独立噪声观测（如开创性方法Noise2Noise所要求的）。此外，盲点网络因其高度空间相关性也无法处理超声散斑噪声。为应对这一挑战，我们提出Speckle2Self——一种仅需单次噪声观测即可实现散斑抑制的新型自监督算法。其核心思想在于：通过多尺度扰动操作在不同尺度上引入散斑模式的组织依赖性变化，同时保留共享的解剖结构。该方法通过将洁净图像建模为低秩信号并分离稀疏噪声分量，实现了有效的散斑抑制。为验证其有效性，我们使用逼真的模拟超声图像和人体颈动脉超声图像，将Speckle2Self与传统基于滤波的去噪算法及基于学习的最新方法进行全面对比。此外，通过采用多台超声设备的数据，评估了模型对未见域图像的泛化能力与适应性。\textit{代码与数据集将在论文录用后公开。}