High resolution medical images are expected for accurate analysis results in medical diagnosis. 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 decomposes 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 better HR medical images than several state-of-the-art SR methods in terms of objective indices and subjective evaluations.

高分辨率医学图像有望在医学诊断中获得准确的分析结果。但是，这些医疗图像的分辨率始终受医疗设备，时间限制等因素限制。尽管有这些限制，但可以使用精心设计的超分辨率（SR）算法来增强这些医学图像的分辨率。作为医学成像后的后处理方式，采用SR算法与升级医疗设备相比具有成本低，效率高的优点。在本文中，我们提出了一个名为LDSRN的网络，该网络结合了拉普拉斯金字塔结构和密集网络，可重建清晰而令人信服的医学HR图像。我们的LDSRN可以充分利用来自不同金字塔等级的信息，以通过密集连接恢复忠实的HR图像。特别，拉普拉斯结构将困难的SR任务分解为几个简单的SR任务，以逐步获取HR图像，以更好地进行重建。实验结果表明，在客观指标和主观评价方面，我们的LDSRN可以比几种最新的SR方法获得更好的HR医学图像。