原子能科学技术2017，Vol.51Issue(5)：890-895,6.DOI:10.7538/yzk.2017.51.05.0890
基于深度学习的辐射图像超分辨率重建方法
Super-resolution Method for Radiation Image Based on Deep Learning
孙跃文 1李立涛 2丛鹏 1向新程 2郭肖静1
作者信息
- 1. 清华大学 核能与新能源技术研究院,北京 100084
- 2. 核检测技术北京市重点实验室,北京 100084
- 折叠
摘要
Abstract
In the security check system, the spatial resolution of radiation image generated by digital radiography is often so low that reduces the image quality.In this work, a super-resolution method based on deep learning was proposed.Using the convolution neural network with residual block, the method trained the radiation image sample in dataset and found the mapping function of low-resolution image to high-resolution image.The experiment result shows that the super-resolution method can deliver superior performance compared with other traditional methods while maintaining an excellent speed.The study result indicates the great potential of deep learning in radiation image processing.关键词
辐射图像/超分辨率重建/深度学习Key words
radiation image/super-resolution/deep learning分类
能源科技引用本文复制引用
孙跃文,李立涛,丛鹏,向新程,郭肖静..基于深度学习的辐射图像超分辨率重建方法[J].原子能科学技术,2017,51(5):890-895,6.
