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. Author manuscript; available in PMC: 2023 Jan 1.
Published in final edited form as: Med Image Anal. 2021 Oct 29;75:102289. doi: 10.1016/j.media.2021.102289

Fig. 3.

Fig. 3.

The network architecture of Attention Residual Dense U-Net (AttRDUNet) used for the image restoration network Gimg and sinogram restoration network Gsino in DuDoDR-Net (Figure 2). Our AttRDUNet consists of Attention Residual Dense Blocks (AttRDB) at different resolution levels, and convolutional GRU (convGRU) in the bottleneck for recurrent learning.