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- W4310584581 abstract "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." @default.
- W4310584581 created "2022-12-12" @default.
- W4310584581 creator A5008812639 @default.
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- W4310584581 date "2022-10-10" @default.
- W4310584581 modified "2023-09-29" @default.
- W4310584581 title "Zero-Shot Learning for Real-Time Ultrasound Image Enhancement" @default.
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- W4310584581 doi "https://doi.org/10.1109/ius54386.2022.9958334" @default.
- W4310584581 hasPublicationYear "2022" @default.
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