  Artifacts, blur and noise are the common distortions degrading MRI images
during the acquisition process, and deep neural networks have been demonstrated
to help in improving image quality. To well exploit global structural
information and texture details, we propose a novel biomedical image
enhancement network, named Feedback Graph Attention Convolutional Network
(FB-GACN). As a key innovation, we consider the global structure of an image by
building a graph network from image sub-regions that we consider to be node
features, linking them non-locally according to their similarity. The proposed
model consists of three main parts: 1) The parallel graph similarity branch and
content branch, where the graph similarity branch aims at exploiting the
similarity and symmetry across different image sub-regions in low-resolution
feature space and provides additional priors for the content branch to enhance
texture details. 2) A feedback mechanism with a recurrent structure to refine
low-level representations with high-level information and generate powerful
high-level texture details by handling the feedback connections. 3) A
reconstruction to remove the artifacts and recover super-resolution images by
using the estimated sub-region correlation priors obtained from the graph
similarity branch. We evaluate our method on two image enhancement tasks: i)
cross-protocol super resolution of diffusion MRI; ii) artifact removal of FLAIR
MR images. Experimental results demonstrate that the proposed algorithm
outperforms the state-of-the-art methods.
