计算机科学
人工智能
计算机视觉
图像质量
散斑噪声
推论
斑点图案
对比度(视觉)
深度学习
图像(数学)
卷积(计算机科学)
模式识别(心理学)
人工神经网络
作者
Yuxuan Li,Wenkai Lu,Patrice Monkam
标识
DOI:10.1109/ius54386.2022.9958334
摘要
Ultrasound(US) imaging has been widely used for clinical diagnosis. However, ultrasound images inherently suffer from speckle noise and low contrast. Although deep learning-based approaches have been proven to outperform traditional filtering algorithms in image enhancement tasks, they usually require high-quality images for training, which are unavailable in practice. In this paper, we develop a zero-shot learning framework for real-time ultrasound image enhancement. In our framework, relatively low-quality(LQ) and high-quality(HQ) images are beamformed using raw Synthetic Aperture Ultrasound (SAU) data with different channel numbers. The generated LQ-HQ image pairs are used to train two designed U-Net variants whereby one is for inference on GPU and the other with depth-wise separable convolution for inference on CPU and mobile devices. The trained models are directly used for enhancing noisy HQ US images without requiring speckle-free and high contrast targets for training. Experiment results indicate that our method performs more favorably against state-of-the-art image enhancement approaches in terms of both image quality and inference time.
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