Noise-Aware Boundary-Enhanced Generative Learning for Ultrasound Speckle Reduction
This paper proposes a Noise-Aware Boundary-Enhanced Generative Learning (NBGL) framework for ultrasound speckle reduction. It integrates a speckle reduction branch and a boundary enhancement branch, with a noise-aware interaction weight generation module that uses 3D Laplacian filtering and median absolute deviation estimation to adapt to varying noise levels. Evaluated on 141 3D transvaginal ultrasound volumes, NBGL outperforms state-of-the-art methods across six noise levels.
[2606.25009] Noise-Aware Boundary-Enhanced Generative Learning for Ultrasound Speckle Reduction
[Submitted on 23 Jun 2026]
Title:Noise-Aware Boundary-Enhanced Generative Learning for Ultrasound Speckle Reduction
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Abstract:Ultrasound is a non-invasive, real-time, and cost-effective imaging technique widely used in clinical diagnosis. However, its diagnostic efficacy is often compromised by inherent speckle noise that degrades image quality and obscures underlying anatomical structures. Existing speckle reduction methods tend to over-smooth tissue boundaries and generalize poorly to heterogeneous noise levels. To address these limitations, we propose a Noise-Aware Boundary-Enhanced Generative Learning (NBGL) framework for ultrasound speckle reduction, which simultaneously preserves annotated anatomical boundaries and adapts to varying noise levels. The NBGL framework consists of a speckle reduction branch and a boundary enhancement branch. The former leverages generative learning to suppress speckle noise, while the latter learns boundary-sensitive representations to preserve target anatomical structures. Furthermore, a noise-aware interaction weight generation (NIWG) module estimates the speckle noise level via 3D Laplacian filtering and a median absolute deviation estimator, and translates it into an adaptive interaction weight. This weight is incorporated into a weighted feature-wise linear modulation (wFiLM) module to adaptively modulate cross-branch feature coupling, thereby improving robustness to varying noise levels. Extensive evaluations on 141 3D transvaginal ultrasound volumes demonstrate that NBGL consistently outperforms state-of-the-art methods in speckle reduction and structural preservation across six noise levels, while maintaining consistency with annotated anatomical boundaries.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.25009 [cs.CV]
(or arXiv:2606.25009v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.25009
arXiv-issued DOI via DataCite
Submission history
From: Yuexi Gu [view email] [v1] Tue, 23 Jun 2026 17:47:13 UTC (2,544 KB)
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