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Denoising medical images by learning sparse image representations with a deep unfolding approach using scan specific metadata
Denoising medical images by learning sparse image representations with a deep unfolding approach using scan specific metadata
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机译:通过使用扫描特定元数据的深度展开方法学习稀疏图像表示来对医学图像进行去噪
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摘要
The present embodiments relate to denoising medical images. By way of introduction, the present embodiments described below include apparatuses and methods for machine learning sparse image representations with deep unfolding and deploying the machine learnt network to denoise medical images. Iterative thresholding is performed using a deep neural network by training each layer of the network as an iteration of an iterative shrinkage algorithm. The deep neural network is randomly initialized and trained independently with a patch-based approach to learn sparse image representations for denoising image data. The different layers of the deep neural network are unfolded into a feed-forward network trained end-to-end.
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