Journal of Shanghai Jiaotong University ›› 2019, Vol. 53 ›› Issue (8): 983-989.doi: 10.16183/j.cnki.jsjtu.2019.08.014

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Improved Residual Encoder-Decoder Network for Low-Dose CT Image Denoising

ZHANG Yungang,YANG Jianfeng,YI Benshun   

  1. Electronic Information School, Wuhan University, Wuhan 430072, China
  • Online:2019-08-28 Published:2019-09-10

Abstract: Aiming at the complex noise and artifacts in low-dose CT images, an improved residual encoder-decoder network for low-dose CT image denoising is proposed. The original residual encoder-decoder network consists of a series of convolution and deconvolution layers, and learns the residuals through shortcut connections. The improvement method mainly includes three aspects: firstly, batch normalization is introduced to improve the denoising performance of the network; secondly, dilated convolution is used to replace normal convolution so as to reduce the parameters in the network without degrading the denoising performance; besides, the number of feature maps in the hidden layer of the network has been adjusted to further optimize the network performance and complexity. Experimental results show that the improved network significantly reduces the complexity and also improves the denoising performance.

Key words: image denoising; low-dose CT; deep learning; convolutional neural network

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