Deep learning techniques have led to state-of-the-art single image
super-resolution (SISR) with natural images. Pairs of high-resolution (HR) and
low-resolution (LR) images are used to train the deep learning model (mapping
function). These techniques have also been applied to medical image
super-resolution (SR). Compared with natural images, medical images have
several unique characteristics. First, there are no HR images for training in
real clinical applications because of the limitations of imaging systems and
clinical requirements. Second, other modal HR images are available (e.g., HR
T1-weighted images are available for enhancing LR T2-weighted images). In this
paper, we propose an unsupervised SISR technique based on simple prior
knowledge of the human anatomy; this technique does not require HR images for
training. Furthermore, we present a guided residual dense network, which
incorporates a residual dense network with a guided deep convolutional neural
network for enhancing the resolution of LR images by referring to different HR
images of the same subject. Experiments on a publicly available brain MRI
database showed that our proposed method achieves better performance than the
state-of-the-art methods.Comment: 10 pages, 3 figure