High-resolution medical images are crucial for medical diagnosis, and for planning and assisting surgery. Micro computed tomography (micro CT) can generate high-resolution 3D images and analyze internal micro-structures. However, micro CT scanners can only scan small objects and cannot be used for in-vivo clinical imaging and diagnosis. In this paper, we propose a super-resolution method to reconstruct micro CT-like images from clinical CT images based on learning a mapping function or relationship between the micro CT and clinical CT. The proposed method consists of following three steps: (1) Pre-processing: This involves the collection of pairs of clinical CT images and micro CT images for training and the registration and normalization of each pair. (2) Training: This involves learning a non-linear mapping function between the micro CT and clinical CT by using training pairs. (3) Processing (testing) step: This involves enhancing a new CT image, which is not included in the training data set, by using the learned mapping function.