Improving resolution of medical images with deep dense convolutional neural network

Doctors always desire high-resolution medical images to have accurate diagnosis. Super-resolution (SR) is a technology that can improve the resolution of medical images. Convolutional neural network (CNN)-based SR methods have achieved desired performance in natural images. In this paper, we apply a deep dense SR (DDSR) convolutional neural networks model to two types of medical images, including Computerized Tomography (CT) images and Magnetic Resonance imaging (MRI) images. This network densely connects every hidden layer to learn high-level features, which was first proposed for object recognition. A set of medical images is used for experiments. We compare the performance of DDSR with three state-of-the-art SR network models, including SR Convolutional Neural Network (SRCNN), Fast SR Convolutional Neural Network (FSRCNN), and Very Deep SR Convolutional Neural Network (VDSR). Both the objective indices and subjective evaluations are used for comparison. The results show that the proposed network has better performances both on CT and MRI images.