To improve the quality of the super-resolution (SR) reconstructed medical images, an improved adaptive multi-dictionary learning method is proposed, which uses the combined information of medical image itself and the natural images database. In training dictionary section, it uses the upper layer images of pyramid which are generated by the self-similarity of low resolution images. In reconstruction section, the top layer image of pyramid is taken as the initial reconstruction image, and medical image’s SR reconstruction is achieved by regularization term which is the non-local structure self-similarity of the image. This method can make full use of the same scale and different scale similar information of medical images. Simulation experiments are carried out on natural images and medical images, and the experimental results show the proposed method is effective for improving the effect of medical image SR reconstruction.

为了提高超分辨率（SR）重建医学图像的质量，提出了一种改进的自适应多字典学习方法，该方法利用医学图像本身和自然图像数据库的组合信息。在训练字典部分中，它使用由低分辨率图像的自相似性生成的金字塔的上层图像。在重建部分，将金字塔的顶层图像作为初始重建图像，并通过正则化项实现医学图像的SR重建，正则化项是图像的非局部结构自相似性。该方法可以充分利用相同比例和不同比例的医学图像相似信息。对自然图像和医学图像进行了模拟实验，