Gated Multi-Attention Feedback Network for Medical Image Super-Resolution

Abstract

Medical imaging technology plays a crucial role in the diagnosis and treatment of diseases. However, the captured medical images are often in a low resolution (LR) due to the limited imaging condition. Super-resolution (SR) technology is a feasible solution to enhance the resolution of a medical image without increasing the hardware cost. However, the existing SR methods often ignore high-frequency details, which results in blurred edges and an unsatisfying visual perception. In this paper, a gated multi-attention feedback network (GAMA) is proposed for medical image SR. Specifically, a gated multi-feedback network is employed as the backbone to extract hierarchical features. Meanwhile, a layer attention feature extraction (LAFE) module is introduced to refine the feature map. In addition, a channel-space attention reconstruction (CSAR) module is built to enhance the representational ability of the semantic feature map. Furthermore, a gradient variance loss is tailored as the regularization in guiding the model learning to regularize the model in generating a faithful high-resolution image with rich textures and sharp edges. The experiments verify the effectiveness of the proposed GAMA compared with the state-of-the-art approaches.