Deep residual neural network based image enhancement algorithm for low dose CT images

Current deep learning based image enhancement algorithms attempt to learn the mapping relationship between degraded images and clear images directly. These algorithms often ignore the fidelity constraint of the observational model. In order to improve the image enhancement performance, an improved deep residual neural network based image enhancement algorithm (DRNN-IE) for low dose CT images is proposed in this paper. DRNN-IE embeds the image enhancement task into a deep neural network, and achieves data consistency using multiple enhancement modules and back-projection modules. The enhancement modules in DRNN-IE produce new features through fusing low-level and high-level features. In order to improve the algorithm's generalization ability, a dual-parameter loss function is adopted to train and optimize the neural network. Experiments on real CT images show that the proposed algorithm has excellent enhancement performance and retains detailed information of low-dose CT images.