CAJ | 학술논문

对厚度差异大的结构件，常规变能量 X 射线图像融合方法不能正确地表征灰度与物理信息复杂的对应关系。为此，本文提出了基于神经网络的 X 射线融合图像灰度修正方法。首先，将常规变能量的融合图像作为神经网络的输入图像，将16位高动态的图像作为相应的输出图像，训练得到 X 射线成像的物理表征模型。然后，利用钢质阶梯块验证方法的正确性与可行性，并将输出结果与16位真实图像进行比较。实验结果表明，该方法很好地拟合了融合图像与真实图像灰度之间的非线性函数关系，扩展了低动态成像采集设备的使用范围。
대후도차이대적결구건，상규변능량 X 사선도상융합방법불능정학지표정회도여물리신식복잡적대응관계。위차，본문제출료기우신경망락적 X 사선융합도상회도수정방법。수선，장상규변능량적융합도상작위신경망락적수입도상，장16위고동태적도상작위상응적수출도상，훈련득도 X 사선성상적물리표정모형。연후，이용강질계제괴험증방법적정학성여가행성，병장수출결과여16위진실도상진행비교。실험결과표명，해방법흔호지의합료융합도상여진실도상회도지간적비선성함수관계，확전료저동태성상채집설비적사용범위。
The conventional X-ray gray weighted image fusion method based on variable energy cannot characterize the phys -ical properties of complicated objects correctly ,therefore ,the gray correction method of X-ray fusion image based on neural network is proposed .The conventional method acquires 12 bit images on variable energy ,and then fuses the images in a tra-ditional way .While the new method takes the fusion image as the input of neural network simulation system and takes the acquired 16 bit image as the output of neural network .The X-ray image physical characteristic model based on neural net-work is obtained through training .And then it takes steel ladder block as the test object to verify the feasibility of the mod -el .In the end ,the gray curve of output image is compared with the gray curve of 16 bit real image .The experiment results show that this method can fit the nonlinear relationship between the fusion image and the real image ,and also can expand the scope of application of low dynamic image acquisition equipment .