DEMIX：用于超声成像混合噪声抑制的双编码潜在掩码框架
DEMIX: Dual-Encoder Latent Masking Framework for Mixed Noise Reduction in Ultrasound Imaging
AI总结：
DEMIX通过双编码潜在掩码框架有效抑制超声图像混合噪声和PSF退化，提升图像质量与结构细节保留。
摘要：
超声成像因其高效、便携性和避免电离辐射而被广泛用于非侵入性医学诊断。然而，其应用受限于信号质量。信号依赖性斑点噪声、信号无关传感器噪声以及由换能器引起的非均匀空间模糊（通过点扩散函数PSF建模）会降低图像质量。这些退化挑战了传统图像修复方法，这些方法假设了简化噪声模型，突显了需要专门算法来有效减少退化并保持细结构细节的必要性。我们提出了DEMIX，一种新的双编码去噪框架，具有掩码门融合机制，用于去除由混合噪声退化的超声图像，并进一步受PSF引起的退化影响。DEMIX受扩散模型启发，具有正向过程和确定性反向过程。DEMIX能够自适应地评估不同的噪声组件，在潜在空间中解耦它们，并在补偿PSF退化的同时抑制这些组件。在两个超声数据集上的广泛实验，以及下游分割任务中，证明了DEMIX在噪声抑制和结构细节保留方面优于最先进的基线方法。代码将公开发布。
英文摘要：
Ultrasound imaging is widely used in noninvasive medical diagnostics due to its efficiency, portability, and avoidance of ionizing radiation. However, its utility is limited by the quality of the signal. Signal-dependent speckle noise, signal-independent sensor noise, and non-uniform spatial blurring caused by the transducer and modeled by the point spread function (PSF) degrade the image quality. These degradations challenge conventional image restoration methods, which assume simplified noise models, and highlight the need for specialized algorithms capable of effectively reducing the degradations while preserving fine structural details. We propose DEMIX, a novel dual-encoder denoising framework with a masked gated fusion mechanism, for denoising ultrasound images degraded by mixed noise and further degraded by PSF-induced distortions. DEMIX is inspired by diffusion models and is characterized by a forward process and a deterministic reverse process. DEMIX adaptively assesses the different noise components, disentangles them in the latent space, and suppresses these components while compensating for PSF degradations. Extensive experiments on two ultrasound datasets, along with a downstream segmentation task, demonstrate that DEMIX consistently outperforms state-of-the-art baselines, achieving superior noise suppression and preserving structural details. The code will be made publicly available.
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