递变能量X射线高动态融合图像的灰度表征算法研究

递变能量X射线成像,通过获取并融合图像序列实现动态范围扩展,完整再现了检测对象的结构信息.但是在融合过程中往往是以质量优化为目的,忽略了与实际高动态成像的灰度映射正确性,从而不能保证图像信息与实际物体信息的物理匹配性.因此,本文提出了递变能量X射线高动态融合图像的灰度表征算法.该算法首先以标钢质准楔形试块为对象,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,利用神经网络方法构建递变能量成像的灰度表征模型.同时针对不同于训练对象的材料,对灰度表征模型进行修正,实现了不同材质的灰度正确表征,进而实现了低动态图像序列融合图像的正确表征.以12和16 bit成像系统进行实验,结果表明,利用12 bit探测器通过变电压采集图像序列,经图像融合、灰度映射及灰度校正,达到了16 bit探测器的成像效果,且满足灰度对应关系,有效拓展了成像器件的动态范围.

Algorithm of high-dynamic fusion image gray characterization based on variable energy

X-ray imaging based on variable energy can expand the dynamic range of the imaging system and perfectly show the structure information of the detection objects, by acquiring and fusing the image sequences. However, the fusion method is ordinarily based on image quality optimization, and neglects the gray mapping accuracy of the actual high dynamic imaging. It cannot guarantee the physical matching between the image information and actual structure information. Therefore, in this paper we propose an X-ray image gray characterization algorithm of high dynamic fusion based on variable energy. First, take a standard wedge block as test object, and use the fusion image of low dynamic image sequences as input data. The output data are the actual high dynamic image. Then establish the X-ray imaging gray characterization model by neural network training. At the same time, because the attenuation coefficients of different heterogeneous materials are different, a modified model of physical characterization is established to achieve a correct characterization of real object. Finally, experiments by 12 bit and 16 bit imaging systems acquire the variable voltage image sequences using 12 bit detector. After image fusion, image mapping and gray level correction, the output image not only achieves the same effect of 16 bit detector, but also satisfies the gray relation. Also this method can effectively expand the dynamic range of the imaging system.
Keywords： variable energy high dynamic gray characterize neural network