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Details of small anatomical landmarks and pathologies, such as small changes of the microvasculature and soft exudates, are critical to accurate disease analysis. However, actual medical images always suffer from limited spatial resolution, due to imaging equipment and imaging parameters (e.g. scanning time of CT images). Recently, machine learning, especially deep learning techniques, have brought revolution to image super resolution reconstruction. Motivated by these achievements, in this paper, we propose a novel super resolution method for medical images based on an improved generative adversarial networks. To obtain useful image details as much as possible while avoiding the fake information in high frequency, the original squeeze and excitation block is improved by strengthening important features while weakening non-important ones. Then, by embedding the improved squeeze and excitation block in a simplified EDSR model, we build a new image super resolution network. Finally, a new fusion loss that can further strengthen the constraints on low-level features is designed for training our model. The proposed image super resolution model has been validated on the public medical images, and the results show that visual effects of the reconstructed images by our method, especially in the case of high upscaling factors, outperform state-of-the-art deep learning-based methods such as SRGAN, EDSR, VDSR and D-DBPN.