基于非局部自回归学习的医学图像超分辨重建方法

doi:10.16451/j.cnki.issn1003-6059.201708009

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Abstract 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.

Key words： Medical Image Super-Resolution Reconstruction Non-local Autoregression ClassificationDictionary

Received: 10 May 2017

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TP 311

About author:: (XU Jun, born in 1993, master student. His research interests include medical image processing and machine learning.)
(YIN Yilong, born in 1972, Ph.D., profe-ssor. His research interests include machine learning and application.)

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201708009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I8/747

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