Abstract: In the field of medical images processing,clear medical images can help doctors to diagnose diseases better.However,due to the limitations of imaging equipment,the generated medical images are often of low resolution and thus may be in appro-priate for diagnosis.Therefore,it is very important to use super-resolution method to improve the image resolution.In recent years,with the development of deep learning,natural image super-resolution methods based on deep learning have been widely studied and achieved promising performance.However,unlike natural image super-resolution,medical image super-resolution often serves downstream medical tasks.The downstream medical tasks,such as disease diagnosis and semantic segmentation,tend to be of interest to certain regions.However,traditional image super-resolution methods often tend to treat all regions in the image equally,without considering the importance of the regions of interest for downstream medical tasks.To tackle this problem,this paper proposes a medical image super-resolution method based on semantic attention.The semantic attention module pays extra attention to the regions of interest in the image by weighting,so that the super-resolution image is more helpful for downstream medical tasks.Experimental results show that the proposed method outperforms other mainstream super-resolution methods on COVID-19 dataset and gastrointestinal polyps dataset Kvasir-SEG.

Key words: Medical image, Super-resolution, Deep learning, Region of interest, Semantic attention