RatUNet: residual U-Net based on attention mechanism for image denoising.

ABSTRACT: Deep convolutional neural networks (CNNs) have been very successful in image denoising. However, with the growth of the depth of plain networks, CNNs may result in performance degradation. The lack of network depth leads to the limited ability of the network to extract image features and difficults to fuse the shallow image features into the deep image information. In this work, we propose an improved deep convolutional U-Net framework (RatUNet) for image denoising. RatUNet improves Unet as follows: (1) RatUNet uses the residual blocks of ResNet to deepen the network depth, so as to avoid the network performance saturation. (2) RatUNet improves the down-sampling method, which is conducive to extracting image features. (3) RatUNet improves the up-sampling method, which is used to restore im

SUBMITTER: Zhang H 

PROVIDER: S-EPMC9138094 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature