X-Ray Energy Self-Adaption High Dynamic Range (HDR) Imaging Based on Linear Constraints With Variable Energy

In X-ray imaging, the image quality is related to the ray energy, imaging detector, object construction, etc. For complex workpieces, if all imaging conditions are identical, not all of the construction projection information can be captured with a fixed energy and limited dynamic range. This will result in over- and underexposed regions, which will affect projection quality. Thus, this paper presents an approach to extend the dynamic range of X-ray imaging systems based on linear constraints with variable energy. First, we study the relationship of grey variation between the adjacent energy images and present the grey gain distribution and linear dispersion. Then, based on a piecewise curve linear approximation, auto-self-adaption variation of ray energy is used to capture the image sequences under the linear constraint of the grey gain distribution. Also, based on the linear assumption, we obtain the weighting fusion coefficient to fuse the image sequences to extend the dynamic range. Finally, experiments demonstrate that this method has an advantage for ray energy auto-control and fusion coefficient calculation, which can realize optimal matching between the X-ray imaging system and the testing object to extend the dynamic range of the imaging system.