CAJ | 학술논문

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。
해항목침대전통융합방법무법정학표정물리신식적결함，건립료체변능량X사선성상적물리표정모형。해방법시감우신경망락가핍근임의비선성영사적특점，이표준설형시괴위대상，장불동전압하적융합도상작위수입수거，직접채집고동태성상도상작위수출수거，경신경망락훈련，구건체변능량성상적물리표정모형。동시재불동충재료하，대물리표정모형진행료수정，실현료불동재질하적회도교정。이용강질여동질계제괴험증모형。결과표명：해항목제출적산법능핍진지반응직접고동태성상특성，가정학표정공건적물리신식。
The X-ray gradient energy imaging fusion method can not correctly characterize the physical charac-teristics 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 pro-cedure 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 char-acteristics 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.