Multimedia Tools and Applications https://doi.org/10.1007/s11042-020-09845-y Medical image super-resolution with laplacian dense network Rui Tang 1 · Lihui Chen 1 · Rongzhu Zhang 1 · Awais Ahmad 2 · Marcelo Keese Albertini 3 · Xiaomin Yang 1 Received: 14 January 2020 / Revised: 8 June 2020 / Accepted: 9 September 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract High resolution medical images are expected for accurate analysis results in medical diag- nosis. However, the resolution of these medical images is always restricted by the factors such as medical devices, time constraints. Despite these restrictions, the resolution of these medical images can be enhanced with a well-designed super-resolution(SR) algorithm. As a post-processing manner after medical imaging, the adoption of the SR algorithms has the advantages of low cost and high efficiency compared with upgrading medical devices. In this paper, we propose a network named LDSRN that combines the Laplacian pyramid structure and the dense network to reconstruct clear and convincing medical HR images. Our LDSRN can make full use of the information from different pyramid levels to recover faithful HR images by the dense connection. Specifically, the Laplacian structure decom- poses the difficult SR task into several easy SR tasks to obtain the HR images step by step for better reconstruction. Experimental results demonstrate that our LDSRN can obtain bet- ter HR medical images than several state-of-the-art SR methods in terms of objective indices and subjective evaluations. Keywords Medical image · Super-resolution · Laplacian pyramid structure · Dense convolutional neural network 1 Introduction High resolution medical images can provide more accurate information compared with the low resolution images to help the doctors conduct accurate analysis. However, the resolution of the medical images is usually limited by medical devices. There are two ways to improve  Xiaomin Yang arielyang@scu.edu.cn 1 College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610064, China 2 Dipartimento di Informatica (DI), Universit` a degli Studi di Milano, Via Celoria 18, Milano MI 20133, Italy 3 Faculty of Computing, Federal University of Uberlandia, Uberlandia, Brazil