基于非局部自回归学习的医学图像超分辨重建方法

摘要

In the process of medical imaging, the image resolution is limited by radiation dose constraints and imaging equipment conditions.The accuracy of late clinical diagnosis and treatment is affected by the low resolution of the medical image.To solve this problem, a medical image super-resolution reconstruction method based on non-local autoregressive learning is proposed.According to the non-local similarity characteristic inherent in medical images, the autoregressive model based on sparse representation is applied to the super-resolution reconstruction process.Furthermore, to improve the efficiency of the experiment, the clustering algorithm is utilized to acquire the classification dictionary.The experimental results demonstrate the feasibility of the proposed method in improving the resolution of medical images as well as the reconstruction efficiency and performance.%由于受放射剂量的影响及成像设备条件的限制,医学图像在成像过程中的分辨率不高,并在一定程度上影响后期临床诊疗的精度.针对此问题,文中提出基于非局部自回归学习的医学图像超分辨重建方法.利用医学图像数据固有的非局部相似性特点,将自回归模型引入到基于稀疏表示的医学图像超分辨重建模型中,同时利用聚类算法得到分类字典,提高实验效率.实验表明,文中方法提高医学图像分辨率方面的可行性,及在重建效率和性能方面的优势.