无需配对数据，一键提升超声视频分辨率与清晰度
Blind Restoration of High-Resolution Ultrasound Video
- 基于自监督学习，通过神经网络自适应优化增强图像
- 在无配对数据下实现超分辨率并有效降噪，性能优于现有方法
- 适合临床超声图像处理，尤其适用于设备差异大的场景
超声成像广泛应用于临床，但超声视频常因信噪比低、分辨率有限而影响诊断。不同设备和采集条件导致数据分布与噪声水平差异大，降低预训练模型的泛化能力。本文提出一种自监督超声视频超分辨率算法 Deep Ultrasound Prior (DUP)，通过神经网络的视频自适应优化，在无需配对训练数据的情况下，同时提升分辨率并去除噪声。定量与视觉评估显示，DUP显著优于现有超分辨率算法，大幅改善下游应用效果。
原文摘要 · Abstract (English)
Ultrasound imaging is widely applied in clinical practice, yet ultrasound videos often suffer from low signal-to-noise ratios (SNR) and limited resolutions, posing challenges for diagnosis and analysis. Variations in equipment and acquisition settings can further exacerbate differences in data distribution and noise levels, reducing the generalizability of pre-trained models. This work presents a self-supervised ultrasound video super-resolution algorithm called Deep Ultrasound Prior (DUP). DUP employs a video-adaptive optimization process of a neural network that enhances the resolution of given ultrasound videos without requiring paired training data while simultaneously removing noise. Quantitative and visual evaluations demonstrate that DUP outperforms existing super-resolution algorithms, leading to substantial improvements for downstream applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。