欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
该项目针对传统融合方法无法正确表征物理信息的缺陷,建立了递变能量X射线成像的物理表征模型。该方法是鉴于神经网络可逼近任意非线性映射的特点,以标准楔形试块为对象,将不同电压下的融合图像作为输入数据,直接采集高动态成像图像作为输出数据,经神经网络训练,构建递变能量成像的物理表征模型。同时在不同种材料下,对物理表征模型进行了修正,实现了不同材质下的灰度校正。利用钢质与铜质阶梯块验证模型。结果表明：该项目提出的算法能逼真地反应直接高动态成像特性,可正确表征工件的物理信息。
英文摘要：
The X-ray gradient energy imaging fusion method can not correctly characterize the physical characteristics of detecting objects.So an X-ray imaging physical characteristic algorithm based on variable energy is proposed in this paper.Because the neural network can approximate any nonlinear mapping correctly,the procedure is to take a standard wedge blocks as test objects,and take the fusion images of the low dynamic images as input data and acquire a high dynamic image directly as desired output data.An X-ray imaging physical characteristic model is built by neural network training.For heterogeneous material,the model of physical characteristics are modified.Steel and copper objects are tested using the physical characteristic model.Experiment shows that the result image can reflect the characteristics of high dynamic image,and can represent the structure information of test objects completely.
同期刊论文项目
同项目期刊论文
期刊信息
位置：