无需干净数据，用多尺度扰动实现超声斑点噪声自监督去噪。
Speckle2Self: Self-Supervised Ultrasound Speckle Reduction Without Clean Data
- 通过多尺度扰动生成不同斑点模式，保留解剖结构
- 将清晰图像建模为低秩信号，分离稀疏噪声成分
- 仅需单个噪声图像，适用于多种超声设备
图像去噪是计算机视觉中的基础任务，尤其在超声成像中，斑点噪声严重降低图像质量。尽管深度神经网络在自然图像去噪上取得显著进展，但其无法直接应用于超声斑点噪声，因其并非纯随机噪声，而是由体内微结构引起的复杂波干涉所致，具有组织依赖性。这使得传统Noise2Noise所需的两个独立噪声观测不可行，且盲区网络也难以处理其高空间相关性。为此，我们提出Speckle2Self，一种仅需单个噪声图像的自监督斑点去除算法。核心思想是：多尺度扰动（MSP）操作在不同尺度引入组织依赖的斑点变化，同时保留共享解剖结构，从而将清洁图像视为低秩信号，分离出稀疏噪声成分。通过与传统滤波方法及前沿学习型方法在真实模拟超声图像和人颈动脉超声图像上的全面对比验证了其有效性，并使用多台超声设备的数据评估模型在未见域下的泛化能力与适应性。
原文摘要 · Abstract (English)
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. Project page: https://noseefood.github.io/us-speckle2self/
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